Automated Detection of Malarial Retinopathy in Digital Fundus Images for Improved Diagnosis in Malawian Children with Clinically Defined Cerebral Malaria

Cerebral malaria (CM), a complication of malaria infection, is the cause of the majority of malaria-associated deaths in African children. The standard clinical case definition for CM misclassifies ~25% of patients, but when malarial retinopathy (MR) is added to the clinical case definition, the specificity improves from 61% to 95%. Ocular fundoscopy requires expensive equipment and technical expertise not often available in malaria endemic settings, so we developed an automated software system to analyze retinal color images for MR lesions: retinal whitening, vessel discoloration, and white-centered hemorrhages. The individual lesion detection algorithms were combined using a partial least square classifier to determine the presence or absence of MR. We used a retrospective retinal image dataset of 86 pediatric patients with clinically defined CM (70 with MR and 16 without) to evaluate the algorithm performance. Our goal was to reduce the false positive rate of CM diagnosis, and so the algorithms were tuned at high specificity. This yielded sensitivity/specificity of 95%/100% for the detection of MR overall, and 65%/94% for retinal whitening, 62%/100% for vessel discoloration, and 73%/96% for hemorrhages. This automated system for detecting MR using retinal color images has the potential to improve the accuracy of CM diagnosis

Sex-partitioning of the <i>Plasmodium falciparum</i> stage V gametocyte proteome provides insight into <i>falciparum</i>-specific cell biology

One of the critical gaps in malaria transmission biology and surveillance is our lack of knowledge about Plasmodium falciparum gametocyte biology, especially sexual dimorphic development and how sex ratios that may influence transmission from the human to the mosquito. Dissecting this process has been hampered by the lack of sex-specific protein markers for the circulating, mature stage V gametocytes. The current evidence suggests a high degree of conservation in gametocyte gene complement across Plasmodium, and therefore presumably for sex-specific genes as well. To better our understanding of gametocyte development and subsequent infectiousness to mosquitoes, we undertook a Systematic Subtractive Bioinformatic analysis (filtering) approach to identify sex-specific P. falciparum NF54 protein markers based on a comparison with the Dd2 strain, which is defective in producing males, and with syntenic male and female proteins from the reanalyzed and updated P. berghei (related rodent malaria parasite) gametocyte proteomes. This produced a short list of 174 male- and 258 female-enriched P. falciparum stage V proteins, some of which appear to be under strong diversifying selection, suggesting ongoing adaptation to mosquito vector species. We generated antibodies against three putative female-specific gametocyte stage V proteins in P. falciparum and confirmed either conserved sex-specificity or the lack of cross-species sex-partitioning. Finally, our study provides not only an additional resource for mass spectrometry-derived evidence for gametocyte proteins but also lays down the foundation for rational screening and development of novel sex-partitioned protein biomarkers and transmission-blocking vaccine candidates

Malarial Diagnosis with Deep Learning and Image Processing Approaches

Malaria is a mosquito-borne disease that has killed an estimated a half-a-million people worldwide since 2000. It may be time consuming and costly to conduct thorough laboratory testing for malaria, and it also requires the skills of trained laboratory personnel. Additionally, human analysis might make mistakes. Integrating denoising and image segmentation techniques with Generative Adversarial Network (GAN) as a data augmentation technique can enhance the performance of diagnosis. Various deep learning models, such as CNN, ResNet50, and VGG19, for recognising the Plasmodium parasite in thick blood smear images have been used. The experimental results indicate that the VGG19 model performed best by achieving 98.46% compared to other approaches. This study demonstrates the potential of artificial intelligence to improve the speed and precision of pathogen detection which is more effective than manual analysis

Development of a label-free Raman imaging technique for differentiation of malaria parasite infected from non-infected tissue

During malarial infection, the host uses the spleen to clear the malaria parasites, however, the parasites have evolved the ability to bind to endothelial receptors in blood vessels of tissues to avoid removal, known as sequestration, and this is largely responsible for the symptoms and severity of infection. So a technique which could non-invasively diagnose tissue burden could be utilised as an aid for localised malaria diagnosis within tissue. Raman spectroscopy is a label-free imaging technique and can provide unique and chemically specific Raman ‘fingerprint’ spectrum of biological samples such as tissue. Within this study, Raman imaging was used to observe the changes to the molecular composition of mice spleen tissue under malarial infection, compared with non-infected samples. From analysis of the Raman imaging data, both tissue types showed very similar spectral profiles, which highlighted that their biochemical compositions were closely linked. Principal component analysis showed very clear separation of the two sample groups, with an associated increase in concentration of heme-based Raman vibrations within the infected dataset. This was indicative of the presence of hemozoin, the malaria pigment, being detected within the infected spleen. Separation also showed that as the hemozoin content within the tissue increased, there was a corresponding change to hemoglobin and some lipid/nucleic acid vibrations. These results demonstrate that Raman spectroscopy can be used to easily discriminate the subtle changes in tissue burden upon malarial infection

Alterations in urine, serum and brain metabolomic profiles exhibit sexual dimorphism during malaria disease progression

<p>Abstract</p> <p>Background</p> <p>Metabolic changes in the host in response to <it>Plasmodium </it>infection play a crucial role in the pathogenesis of malaria. Alterations in metabolism of male and female mice infected with <it>Plasmodium berghei </it>ANKA are reported here.</p> <p>Methods</p> <p>¹H NMR spectra of urine, sera and brain extracts of these mice were analysed over disease progression using Principle Component Analysis and Orthogonal Partial Least Square Discriminant Analysis.</p> <p>Results</p> <p>Analyses of overall changes in urinary profiles during disease progression demonstrate that females show a significant early post-infection shift in metabolism as compared to males. In contrast, serum profiles of female mice remain unaltered in the early infection stages; whereas that of the male mice changed. Brain metabolite profiles do not show global changes in the early stages of infection in either sex. By the late stages urine, serum and brain profiles of both sexes are severely affected. Analyses of individual metabolites show significant increase in lactate, alanine and lysine, kynurenic acid and quinolinic acid in sera of both males and females at this stage. Early changes in female urine are marked by an increase of ureidopropionate, lowering of carnitine and transient enhancement of asparagine and dimethylglycine. Several metabolites when analysed individually in sera and brain reveal significant changes in their levels in the early phase of infection mainly in female mice. Asparagine and dimethylglycine levels decrease and quinolinic acid increases early in sera of infected females. In brain extracts of females, an early rise in levels is also observed for lactate, alanine and glycerol, kynurenic acid, ureidopropionate and 2-hydroxy-2-methylbutyrate.</p> <p>Conclusions</p> <p>These results suggest that <it>P. berghei </it>infection leads to impairment of glycolysis, lipid metabolism, metabolism of tryptophan and degradation of uracil. Characterization of early changes along these pathways may be crucial for prognosis and better disease management. Additionally, the distinct sexual dimorphism exhibited in these responses has a bearing on the understanding of the pathophysiology of malaria.</p

Mining, visualizing and comparing multidimensional biomolecular data using the Genomics Data Miner (GMine) web-server

Genomics Data Miner (GMine) is a user-friendly online software that allows non-experts to mine, cluster and compare multidimensional biomolecular datasets. Various powerful visualization techniques are provided, generating high quality figures that can be directly incorporated into scientific publications. Robust and comprehensive analyses are provided via a broad range of data-mining techniques, including univariate and multivariate statistical analysis, supervised learning, correlation networks, clustering and multivariable regression. The software has a focus on multivariate techniques, which can attribute variance in the measurements to multiple explanatory variables and confounders. Various normalization methods are provided. Extensive help pages and a tutorial are available via a wiki server. Using GMine we reanalyzed proteome microarray data of host antibody response against Plasmodium falciparum. Our results support the hypothesis that immunity to malaria is a higher-order phenomenon related to a pattern of responses and not attributable to any single antigen. We also analyzed gene expression across resting and activated T cells, identifying many immune-related genes with differential expression. This highlights both the plasticity of T cells and the operation of a hardwired activation program. These application examples demonstrate that GMine facilitates an accurate and in-depth analysis of complex molecular datasets, including genomics, transcriptomics and proteomics data

Treatment of Vivax Malaria in Vietnam

Globally, relapse caused by Plasmodium vivax malaria is estimated to contribute approximately 50% to the overall number of vivax infections. In South-East Asia, relapse rates commonly exceed 50%, making relapse as the main source of vivax illness. Recurrent episodes of febrile illness and hemolysis inflict a significant public health burden particularly in vulnerable groups such as pregnant women and young children. Multiple relapses may contribute substantially to the delayed morbidity and unappreciated mortality relative to falciparum malaria. Therefore, the radical cure of P. vivax malaria requires a combination of both blood schizontocides to achieve acute clinical cure, and hypnozoiticides to prevent relapses in the future. The World Health Organization (WHO) recommends the standard 14-day primaquine regimen in curing and preventing relapse from vivax malaria. Another 8-aminoquinoline derivative with prolonged elimination half-life, tafenoquine, also propose an alternative choice in radical therapy. The main research question is: what is the most effective treatment to achieve radical cure of Plasmodium vivax in Vietnam? In order to answer this question a series of studies and clinical trials were conducted in Vietnam and our findings point to these conclusions: 1/ The short 7-day course of primaquine is an available, safe and highly efficacious antirelapse treatment that could improve the adherence of patients and hence the effectiveness of radical cure of vivax relapse. 2/ The single dose of 300 mg tafenoquine, an 8-aminoquinoline drug, has similar antirelapse activity compared to primaquine. When administered with chloroquine, this hypnozoiticidal agent is a potential candidate in the malaria elimination era. 3/ The prevalence and variants of G6PD deficiency determinants of hemolytic toxicity of 8-aminoquinolines in malaria endemic regions represent important factors to be considered when implementing radical cure of latent vivax malaria

An ICA-ensemble learning approaches for prediction of RNA-seq malaria vector gene expression data classification

Malaria parasites introduce outstanding life-phase variations as they grow across multiple atmospheres of the mosquito vector. There are transcriptomes of several thousand different parasites. (RNA-seq) Ribonucleic acid sequencing is a prevalent gene expression tool leading to better understanding of genetic interrogations. RNA-seq measures transcriptions of expressions of genes. Data from RNA-seq necessitate procedural enhancements in machine learning techniques. Researchers have suggested various approached learning for the study of biological data. This study works on ICA feature extraction algorithm to realize dormant components from a huge dimensional RNA-seq vector dataset, and estimates its classification performance, Ensemble classification algorithm is used in carrying out the experiment. This study is tested on RNA-Seq mosquito anopheles gambiae dataset. The results of the experiment obtained an output metrics with a 93.3% classification accuracy