Unilamellar lipid vesicles can serve as model for protocells. We present a vesicle fission mechanism in a thermal gradient under flow in a convection chamber, where vesicles cycle cold and hot regions periodically. Crucial to obtain fission of the vesicles in this scenario is a temperature-induced membrane phase transition that vesicles experience multiple times. We model the temperature gradient of the chamber with a capillary to study single vesicles on their way through the temperature gradient in an external field of shear forces. Starting in the gel-like phase the spherical vesicles are heated above their main melting temperature resulting in a dumbbell-deformation. Further downstream a temperature drop below the transition temperature induces splitting of the vesicles without further physical or chemical intervention. This mechanism also holds for less cooperative systems, as shown here for a lipid alloy with a broad transition temperature width of 8 K. We find a critical tether length that can be understood from the transition width and the locally applied temperature gradient. This combination of a temperature-induced membrane phase transition and realistic flow scenarios as given e.g. in a white smoker enable a fission mechanism that can contribute to the understanding of more advanced protocell cycles.
Although malaria has been known for more than 4 thousand years1, it still imposes a global burden with approx. 240 million annual cases2. Improvement in diagnostic techniques is a prerequisite for its global elimination. Despite its main limitations, being time-consuming and subjective, light microscopy on Giemsa-stained blood smears is still the gold-standard diagnostic method used worldwide. Autonomous computer assisted recognition of malaria infected red blood cells (RBCs) using neural networks (NNs) has the potential to overcome these deficiencies, if a fast, high-accuracy detection can be achieved using low computational power and limited sets of microscopy images for training the NN. Here, we report on a novel NN-based scheme that is capable of the high-speed classification of RBCs into four categories—healthy ones and three classes of infected ones according to the parasite age—with an accuracy as high as 98%. Importantly, we observe that a smart reduction of data dimension, using characteristic one-dimensional cross-sections of the RBC images, not only speeds up the classification but also significantly improves its performance with respect to the usual two-dimensional NN schemes. Via comparative studies on RBC images recorded by two additional techniques, fluorescence and atomic force microscopy, we demonstrate that our method is universally applicable for different types of microscopy images. This robustness against imaging platform-specific features is crucial for diagnostic applications. Our approach for the reduction of data dimension could be straightforwardly generalised for the classification of different parasites, cells and other types of objects.
Efficient malaria treatment is a major healthcare challenge. Addressing this challenge requires in-depth understanding of malaria parasite maturation during the intraerythrocytic cycle. Exploring the structural and functional changes of the parasite through the intraerythrocytic stages and their impact on red blood cells (RBCs) is a cornerstone of antimalarial drug development. In order to precisely trace such changes, we performed a thorough imaging study of RBCs infected by Plasmodium falciparum, by using atomic force microscopy (AFM) and total internal reflection fluorescence microscopy (TIRF) supplemented with bright field microscopy for stage assignment. This multifaceted imaging approach allows to reveal structure–function relations via correlations of the parasite maturation with morphological and fluorescence properties of the stages. We established diagnostic patterns characteristic to the parasite stages based on the topographical profile of infected RBCs, which show close correlation with their fluorescence (TIRF) map. Furthermore, we found that hemozoin crystals exhibit a strong optical contrast, possibly due to the quenching of fluorescence. The topographical and optical features provide a tool for locating the hemozoin crystals within the RBCs and following their growth.
Due to climate change and the COVID-19 pandemic, the number of malaria cases and deaths increased between 2019 and 2020. Reversing this trend and eliminating malaria worldwide requires improvements in malaria diagnosis, in which artificial intelligence (AI) has recently been demonstrated to have a great potential. Here, we describe an AI-based approach that boosts the performance of light (LM), atomic force (AFM) and fluorescence microscopy (FM)-based malaria diagnosis. As the main challenge, the stage-specific recognition of infected red blood cells (RBCs) usually requires large sets of microscopy images for training a neural network, which is difficult to obtain. Our tool, the Malaria Stage Classifier, provides a fast, high-accuracy recognition that works even with limited training sets due to a smart reduction of data dimension. Individual RBCs are extracted from an image, reduced to characteristic one-dimensional cross-sections, and classified. We show that our method is applicable to images recorded by various microscopy techniques. It is available as a software package at https://github.com/KatharinaPreissinger/Malaria_stage_classifier and can be used within a python environment. Technical support is provided by the corresponding author (katharina.preissinger@physik.uni-augsburg.de).
Although malaria has been known for more than 4 thousand years, it still imposes a global burden with approx. 240 million annual cases. Improvement in diagnostic techniques is a prerequisite for its global elimination. Despite its main limitations, being time-consuming and subjective, light microscopy on Giemsa-stained blood smears is still the gold-standard diagnostic method used worldwide. Autonomous computer assisted recognition of malaria infected red blood cells (RBCs) using neural networks (NNs) has the potential to overcome these deficiencies, if a fast, high-accuracy detection can be achieved using low computational power and limited sets of microscopy images for training the NN. Here, we report on a novel NN-based scheme that is capable of the high-speed classification of RBCs into four categories---healthy ones and three classes of infected ones according to the parasite age---with an accuracy as high as 98%. Importantly, we observe that a smart reduction of data dimension, using characteristic one-dimensional cross-sections of the RBC images, not only speeds up the classification but also significantly improves its performance with respect to the usual two-dimensional NN schemes. Via comparative studies on RBC images recorded by two additional techniques, fluorescence and atomic force microscopy, we demonstrate that our method is universally applicable for different types of microscopy images. This robustness against imaging platform-specific features is crucial for diagnostic applications. Our approach for the reduction of data dimension can be straightforwardly generalised for the classification of different parasites, cells and other types of objects.
Efficient malaria treatment is a global challenge, requiring in-depth view into the maturation of malaria parasites during the intraerythrocytic cycle. Exploring structural and functional variations of the parasites through the intraerythrocytic stages and their impact on red blood cell (RBCs) is a cornerstone of antimalarial drug development. In order to trace such changes in fine steps of parasite development, we performed an imaging study of RBCs infected by Plasmodium falciparum, using atomic force microscopy (AFM) and total internal reflection fluorescence microscopy (TIRF), further supplemented with bright field microscopy for the direct assignment of the stages. This multifaceted imaging approach allows to reveal structure–functionality relations via correlations of the parasite maturation with morphological and fluorescence properties of the stages. We established identification patterns characteristic to the different parasite stages based on the height profile of infected RBCs, as obtained by AFM, which show close correlation with typical fluorescence (TIRF) maps of RBCs. Furthermore, we found that hemozoin crystals exhibit a strong optical contrast by quenching fluorescence. We demonstrate that these topographic and optical features also provide a tool to locate the hemozoin crystals within the RBCs and, in turn, to follow their growth.
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