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Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks.

ABSTRACT: The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.

SUBMITTER: Kittichai V

PROVIDER: S-EPMC8376898 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMalaria continues to be one of the most severe global infectious diseases, responsible for 1-2 million deaths yearly. The rapid evolution and spread of drug resistance in parasites has led to an urgent need for the development of novel antimalarial targets. Proteases are a group of enzymes that play essential roles in parasite growth and invasion. The possibility of designing specific inhibitors for proteases makes them promising drug targets. Previously, combining a comparative genomics approach and a machine learning approach, we identified the complement of proteases (degradome) in the malaria parasite Plasmodium falciparum and its sibling species 123, providing a catalog of targets for functional characterization and rational inhibitor design. Network analysis represents another route to revealing the role of proteins in the biology of parasites and we use this approach here to expand our understanding of the systems involving the proteases of P. falciparum.ResultsWe investigated the roles of proteases in the parasite life cycle by constructing a network using protein-protein association data from the STRING database 4, and analyzing these data, in conjunction with the data from protein-protein interaction assays using the yeast 2-hybrid (Y2H) system 5, blood stage microarray experiments 678, proteomics 9101112, literature text mining, and sequence homology analysis. Seventy-seven (77) out of 124 predicted proteases were associated with at least one other protein, constituting 2,431 protein-protein interactions (PPIs). These proteases appear to play diverse roles in metabolism, cell cycle regulation, invasion and infection. Their degrees of connectivity (i.e., connections to other proteins), range from one to 143. The largest protease-associated sub-network is the ubiquitin-proteasome system which is crucial for protein recycling and stress response. Proteases are also implicated in heat shock response, signal peptide processing, cell cycle progression, transcriptional regulation, and signal transduction networks.ConclusionsOur network analysis of proteases from P. falciparum uses a so-called guilt-by-association approach to extract sets of proteins from the proteome that are candidates for further study. Novel protease targets and previously unrecognized members of the protease-associated sub-systems provide new insights into the mechanisms underlying parasitism, pathogenesis and virulence.

Project description:PURPOSE:Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this paper, we developed a method for classifying subtypes of lung adenocarcinoma from pathological images using neural network whose that can evaluate phenotypic features from wider area to consider cellular distributions. METHODS:In order to recognize the types of tumors, we need not only to detail features of cells, but also to incorporate statistical distribution of the different types of cells. Variants of autoencoders as building blocks of pre-trained convolutional layers of neural networks are implemented. A sparse deep autoencoder which minimizes local information entropy on the encoding layer is then proposed and applied to images of size [Formula: see text]. We applied this model for feature extraction from pathological images of lung adenocarcinoma, which is comprised of three transcriptome subtypes previously defined by the Cancer Genome Atlas network. Since the tumor tissue is composed of heterogeneous cell populations, recognition of tumor transcriptome subtypes requires more information than local pattern of cells. The parameters extracted using this approach will then be used in multiple reduction stages to perform classification on larger images. RESULTS:We were able to demonstrate that these networks successfully recognize morphological features of lung adenocarcinoma. We also performed classification and reconstruction experiments to compare the outputs of the variants. The results showed that the larger input image that covers a certain area of the tissue is required to recognize transcriptome subtypes. The sparse autoencoder network with [Formula: see text] input provides a 98.9% classification accuracy. CONCLUSION:This study shows the potential of autoencoders as a feature extraction paradigm and paves the way for a whole slide image analysis tool to predict molecular subtypes of tumors from pathological features.

Project description:BACKGROUND: The carbon metabolism of the blood stages of Plasmodium falciparum, comprising rapidly dividing asexual stages and non-dividing gametocytes, is thought to be highly streamlined, with glycolysis providing most of the cellular ATP. However, these parasitic stages express all the enzymes needed for a canonical mitochondrial tricarboxylic acid (TCA) cycle, and it was recently proposed that they may catabolize glutamine via an atypical branched TCA cycle. Whether these stages catabolize glucose in the TCA cycle and what is the functional significance of mitochondrial metabolism remains unresolved. RESULTS: We reassessed the central carbon metabolism of P. falciparum asexual and sexual blood stages, by metabolically labeling each stage with 13C-glucose and 13C-glutamine, and analyzing isotopic enrichment in key pathways using mass spectrometry. In contrast to previous findings, we found that carbon skeletons derived from both glucose and glutamine are catabolized in a canonical oxidative TCA cycle in both the asexual and sexual blood stages. Flux of glucose carbon skeletons into the TCA cycle is low in the asexual blood stages, with glutamine providing most of the carbon skeletons, but increases dramatically in the gametocyte stages. Increased glucose catabolism in the gametocyte TCA cycle was associated with increased glucose uptake, suggesting that the energy requirements of this stage are high. Significantly, whereas chemical inhibition of the TCA cycle had little effect on the growth or viability of asexual stages, inhibition of the gametocyte TCA cycle led to arrested development and death. CONCLUSIONS: Our metabolomics approach has allowed us to revise current models of P. falciparum carbon metabolism. In particular, we found that both asexual and sexual blood stages utilize a conventional TCA cycle to catabolize glucose and glutamine. Gametocyte differentiation is associated with a programmed remodeling of central carbon metabolism that may be required for parasite survival either before or after uptake by the mosquito vector. The increased sensitivity of gametocyte stages to TCA-cycle inhibitors provides a potential target for transmission-blocking drugs.

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Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks.

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