This work (Research grant for New Scholar, Grant No. RGNS 65 - 212) was financially supported by the Office of the Permanent Secretary, Ministry of Higher Education, Science, Research and Innovation (OPS MHESI), Thailand Science Research and Innovation (TSRI) and King Mongkut&#39;s Institute of Technology Ladkrabang. We are grateful to the National Research Council of Thailand (NRCT) [NRCT5-RSA63001-10] for funding the research project. M.K. was funded by Thailand Science Research and Innovation Fund Chulalongkorn University. We also thank the College of Advanced Manufacturing Innovation, King Mongkut&#39;s Institute of Technology, Ladkrabang who have provided the deep learning platform and software to support the research project.

AI Program for Trypanosomiasis Detection and Classification

All authors have no financial disclosures and no conflicts of interest.

Microscopic observation for Trypanosoma protozoa infection is early and commonly used, especially during surveillance in remote areas where there is a lack of skilled technicians and labor-intensive and time-consuming processes that are all obstacles to reporting the health organization timely. Although molecular biology techniques such as immunology and polymerase chain reaction (PCR) have been approved as high-sensitivity methods to support the effectiveness of lab findings, expensive chemicals, apparatus, and professi...

Trypanosomiasis is a significant challenge to global health issues due to a variety of zoonotic species causing human disease with a wide range of geographical distribution outside the African and American continents, such as South and Southeast Asia1,2,3. Human African trypanosomiasis (HAT) or sleeping sickness, is caused by Trypanosoma brucei gambiense and T. b. rhodesiense which produce the chronic and acute forms, respectively, representing the major spread in Africa. The causative parasite belongs to the Salivaria group due to the transmission by infected saliva of Tsetse flies4. Whereas, the well-known American trypanosomiasis (Chagas&#39;s disease) caused by T. cruzi has been a public health concern for non-endemic countries; including Canada, the USA, Europe, Australia, and Japan, because of the frequent migration of individuals from endemic areas5. The trypanosome infection belongs to the Stercoraria group because it is transmitted by the infected feces of reduviid bugs. The trypanosomiases and trypanosomoses (Surra disease) caused by the T. evansi infection are endemic in Africa, South America, Western and Eastern Asia, and South and Southeast Asian countries3,6. Although human trypanosomiasis caused by the trypanosome has been reported3,4,7,8,9,10,11,12, the route of transmission of the parasite infection is debated: either the mechanical or infected blood through hematophagous insects such as tsetse flies and tabanids or horse flies6,7,8,9,10,12,13,14. No case report has been found in Thailand, however, a high prevalence of the T. evansi infection in dog15, racing horses, and water buffalo in the eastern region has been published16, suggesting an acquired transmission between domestic animals would have occurred. Several atypical human infections caused by animal trypanosomes (T. vivax, T. b. brucei, T. congolense, T. lewisi, and T. evansi) were reported, which are not the classical forms of human trypanosomes17. Awareness about atypical human infections might be underestimated, highlighting the need for improved diagnostic tests and field investigations for detection and confirmation of these atypical cases, and allowing for proper control and treatment of animal pathogenic diseases that affect global livestock, food security18, and human healthcare. This led to the development of a potential strategy integrated with an existing common method (microscopic examination) to rapidly screen blood samples in remote areas during active surveillance, enabling the identification of the hotspot zones for restricting and controlling the disease.
Having a sporadic incidence of the Surra disease in a wide range of domestic animals such as dromedaries, cattle, equines, and dogs that evoke a euryxenous T. evansi may be zoonotic to humans1,4,13,14. Human infection seems impossible because a trypanolytic factor in human serum, expressed from a sra-like gene, is capable of preventing human T. brucei and T. congolense12,19. Furthermore, as the first case report from India demonstrates, the illness has no association with immunocompromised HIV patients4. As described above, the possible human infection may be related to a high-density lipoprotein deficiency with abnormal function of the trypanosome lytic factor, which is a rare autosomal recessive genetic disorder, namely Tangier disease4. In 2016, a Vietnamese patient was discovered to possess two wild-type APOL1 alleles and a serum APOL1 concentration within the normal range. However, the theory of APOL-1 deficiency is no longer considered valid12. Therefore, one possible mechanism of trypanosome infection is direct contact of a wound with infected animal blood during occupational animal farming4,12. Microscopic examination reveals that T. evansi morphology is a monomorphic form of the trypomastigote including a predominant long slender, flagellated, and dividing trypanosome which is similar to their relative species of T. brucei1,12,13. The nucleus is in the central position with a visible small kinetoplast in the posterior position. A previous study indicated that the parasite can exist in two comparable forms, known as the classical and truncated forms. However, it remains necessary to confirm their respective pathogenic effects on hosts20. The course of symptoms varies ranging from intermittent fever associated with chills and sweating. Suramin, fortunately, is a successful first-line therapy for early-stage human African trypanosomiasis with no invasion of the central nervous system (CNS), healing patients in India and Vietnam4,12,21.
Except for clinical sign examination, several diagnostic methods for T. evansi parasites exist, including parasitological microscopic observation4,9,12, serological4,8,9,10,12, and molecular biological tests4,12. Thin-blood films stained with Giemsa are often used to visualize the parasite present under microscopic examination, which is routinely and commonly used22. However, the procedure appears to be feasible; nonetheless, it is time-consuming and labor-intensive, has inter-rater assessment variability, is sensitive to only an acute phase, and requires a personal trainee23. Both molecular biology and serological testing also needed highly skilled personnel to perform multiple processes of sample preparation, including extracting and purifying the samples before testing them with expensive apparatus, which is difficult to standardize, risk of contamination with extra-parasitic materials, and discrepancies in results24. Based on the rationale described above, rapid and early screening technology is needed to support the field surveillance study and ensure that the survey result is reported in a timely manner to identify the hotspot zone for further control of the disease transmission1,8. Computerized-based devices (CAD) have been proposed as an innovative technology for medical fields, including histopathological and cytopathological tasks25. The CAD mentioned above was performed at high speed and computed using pattern recognition, namely, artificial intelligence (AI). The AI method is accomplished using convolutional neural network algorithms that can be used to deal with a large number of dataset samples, especially, a supervised learning approach that trains a well-trained model upon data consumption.
In general, AI is the ability of computers to solve tasks that require expert intelligence, such as data labeling. Machine learning (ML), a subfield of AI, is represented as a computer system with two different processes comprised of feature extraction and pattern recognition. Deep learning (DL), or advanced ML algorithms, refers to the development of computerized programs and devices comparing human-like performance with levels of accuracy greater and equal to that accomplished by human professionals26. Currently, the role of DL in medical and veterinary fields is promisingly expanding and revolutionizing communicable disease prevention with the aim of recent prevention and guiding it to individual health staff22,27. The potential DL application is limitless with quality labels and a large number of augmented datasets, freeing specialists to manage the project task. Specifically, an advance in the digital image along with computer-assisted analysis, improved the automatic diagnostic and screening in five categories of pathology reported; including static, dynamic, robotic, whole slide imaging, and hybrid methods28. It is necessary to consider that the integration of DL algorithm approaches and digital image data could encourage local staff to utilize the technology in their daily practices.
Previously, the increase in prediction accuracy of using a hybrid model had been proven27. To identify the trypanosome parasite in microscopic images, this research presents two hybrid models, incorporating the YOLOv4-tiny (object detection) and Densenet201 (object classification) algorithms. Among several detection models, YOLOv4-tiny with a CSPDarknet53 backbone showed high performance as a prediction result in terms of localization and classification29. Since the real-time detector has modified the optimal balance among the input network resolution, the amount of the convolutional layer, the total parameter, and the number of layer outputs, it has improved prioritizing fast operating speeds and optimizing for parallel computations when compared to previous versions. Dense Convolutional Network (DenseNet) is another popular model that achieves state-of-the-art results across competitive datasets. DenseNet201 yielded a similar validation error comparable to that of ResNet101; however, DenseNet201 has fewer than 20 million parameters, which is less than ResNet101&#39;s more than 40 million parameters30. Therefore, the DenseNet model could improve prediction accuracy with an increasing number of parameters with no sign of overfitting. Here, an artificial intelligence (AI) program utilizes a hybrid deep learning algorithm with deep detection- and classification neural network backbones on the in-house CiRA CORE platform. The developed program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. This technology has the potential to revolutionize disease surveillance and control by providing a rapid, automated, and accurate screening method. It could aid local staff in making more informed decisions on transmission-blocking strategies for parasitic protozoan disease.

<table><tbody><tr><td>Darknet19, Darknet53 and Densenet201</td><td>Gao Huang, Z. L., Laurens van der Maaten. Densely Connected Convolutional Networks. arXiv:1608.06993 [cs.CV]. (2016)</td><td>https://github.com/liuzhuang13/DenseNet&amp;nbsp;</td><td>Deep convolutional neural network model that can function to&amp;nbsp; classification&lt;br/&gt; &lt;strong&gt;Generic name&lt;/strong&gt;: YOLO model/ detection model?</td></tr><tr><td>Olympus CX31 Model CX31RRBSFA</td><td>&amp;nbsp;Olympus, Tokyo, Japan</td><td>SN 4G42178&amp;nbsp;</td><td>A light microscope&amp;nbsp;</td></tr><tr><td>Olympus DP21-SAL U-TV0.5XC-3</td><td>&amp;nbsp;Olympus, Tokyo, Japan</td><td>SN 3D03838</td><td>A digital camera&lt;br/&gt; &lt;strong&gt;Generic name&lt;/strong&gt;: Classification models/ densely CNNs</td></tr><tr><td>Window 10</td><td>Microsoft</td><td>Window 10</td><td>Operation system in computers</td></tr><tr><td>YOLO v4-tiny&amp;nbsp;</td><td>Naing, K. M. et al. Automatic recognition of parasitic products in stool examination using object detection approach. PeerJ Comput Sci. 8 e1065, (2022).</td><td>https://git.cira-lab.com/users/sign_in</td><td>Deep convolutional neural network model that can function to both localization and also classification&amp;nbsp;</td></tr><tr><td></td><td></td><td>https://git.cira-lab.com/users/sign_in</td><td></td></tr></tbody></table>

superior auto-identification of trypanosome parasites by using a hybrid deep-learning model

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method. It is, nevertheless, primarily reliant on skilled and experienced personnel. To address this issue, an artificial intelligence (AI) program was introduced that makes use of a hybrid deep learning technique of object identification and object classification neural network backbones on the in-house low-code AI platform (CiRA CORE). The program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. The AI program utilizes pattern recognition to observe and analyze multiple protozoa within a single blood sample and highlights the nucleus and kinetoplast of each parasite as specific characteristic features using an attention map.
To assess the AI program&#39;s performance, two unique modules are created that provide a variety of statistical measures such as accuracy, recall, specificity, precision, F1 score, misclassification rate, receiver operating characteristics (ROC) curves, and precision versus recall (PR) curves. The assessment findings show that the AI algorithm is effective at identifying and categorizing parasites. By delivering a speedy, automated, and accurate screening tool, this technology has the potential to transform disease surveillance and control. It could also assist local officials in making more informed decisions on disease transmission-blocking strategies.

In this study, hybrid deep learning algorithms were proposed to help automatically predict the positivity of a blood sample with a trypanosome parasite infection. Archived, Giemsa-stained blood films were sorted to localize and classify the parasitized versus non-parasitic by using the object detection algorithm based on a darknet backbone neural network. Within any rectangular box prediction result obtained by the previous model, the best-selected classification model was developed to classify all three species of medic...

Watch this Scientific Journal Video about AI-Driven Trypanosome Species Detection from Microscopic Images at JoVE.com

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images 

Worldwide medical blood parasites were automatically screened using simple steps on a low-code AI platform. The prospective diagnosis of blood films was improved by using an object detection and classification method in a hybrid deep learning model. The collaboration of active monitoring and well-trained models helps to identify hotspots of trypanosome transmission.

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of ...

superior-auto-identification-trypanosome-parasites-using-hybrid-deep

Research

JoVE Journal

Bioengineering

1.3K Views.  King Mongkut’s Institute of Technology Ladkrabang. Het doel van het onderzoek is om een AI-model met kunstmatige intelligentie te ontwikkelen dat gebruik maakt van een hybride deep learning-techniek van objectdetectie en classificatie-ruggengraat voor het definiëren van de protozoa-trypanosoomsoort, namelijk trypanosoma cruzi, T.brucei en T.evansi, uit olie-immersie, microscopische beelden op het in-house, low-code AI-platform CiRA CORE. De meest recente ontwikkeling in ons onderzoeksveld is de ontwikkeling van de leeralgoritmen die trypanos...

Video: Auteur in de schijnwerpers: AI-gestuurde detectie van trypanosoomsoorten op basis van microscopische afbeeldingen 

Measurement of Aggregate Cohesion by Tissue Surface Tensiometry

Rigorous measurement of intercellular binding energy can only be made using methods grounded in thermodynamic principles in systems at equilibrium. We have developed tissue surface tensiometry (TST) specifically to measure the surface free energy of interaction between cells. The biophysical concepts underlying TST have been previously described in detail1,2. The method is based on the observation that mutually cohesive cells, if maintained in shaking culture, will spontaneously assemble into clusters. Over time, these clusters will round up to form spheres. This rounding-up behavior mimics the behavior characteristic of liquid systems. Intercellular binding energy is measured by compressing spherical aggregates between parallel plates in a custom-designed tissue surface tensiometer. The same mathematical equation used to measure the surface tension of a liquid droplet is used to measure surface tension of 3D tissue-like spherical aggregates. The cellular equivalent of liquid surface tension is intercellular binding energy, or more generally, tissue cohesivity. Previous studies from our laboratory have shown that tissue surface tension (1) predicts how two groups of embryonic cells will interact with one another1-5, (2) can strongly influence the ability of tissues to interact with biomaterials6, (3) can be altered not only through direct manipulation of cadherin-based intercellular cohesion7, but also by manipulation of key ECM molecules such as FN8-11 and 4) correlates with invasive potential of lung cancer12, fibrosarcoma13, brain tumor14 and prostate tumor cell lines15. In this article we will describe the apparatus, detail the steps required to generate spheroids, to load the spheroids into the tensiometer chamber, to initiate aggregate compression, and to analyze and validate the tissue surface tension measurements generated.

We describe a method of measuring binding energy, expressible as tissue surface tension, between cells within 3D tissue-like aggregates. Differences in tissue surface tension have been demonstrated to correlate with invasiveness of lung, muscle, and brain tumors, and are fundamental determinants of establishing spatial relationships between different cell types.

Rigorous measurement of intercellular binding energy can only be made using methods grounded in thermodynamic principles in systems at equilibrium.  We ...

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

The fluid-structure energy exchange process for normal speech has been studied extensively, but it is not well understood for pathological conditions. Polyps and nodules, which are geometric abnormalities that form on the medial surface of the vocal folds, can disrupt vocal fold dynamics and thus can have devastating consequences on a patient&#39;s ability to communicate. Our laboratory has reported particle image velocimetry (PIV) measurements, within an investigation of a model polyp located on the medial surface of an in&#160;vitro driven vocal fold model, which show that such a geometric abnormality considerably disrupts the glottal jet behavior. This flow field adjustment is a likely reason for the severe degradation of the vocal quality in patients with polyps. A more complete understanding of the formation and propagation of vortical structures from a geometric protuberance, such as a vocal fold polyp, and the resulting influence on the aerodynamic loadings that drive the vocal fold dynamics, is necessary for advancing the treatment of this pathological condition. The present investigation concerns the three-dimensional flow separation induced by a wall-mounted prolate hemispheroid with a 2:1 aspect ratio in cross flow, i.e. a model vocal fold polyp, using an oil-film visualization technique. Unsteady, three-dimensional flow separation and its impact of the wall pressure loading are examined using skin friction line visualization and wall pressure measurements.

Vocal fold polyps can disrupt vocal fold dynamics and thus can have devastating consequences on a patient&#39;s ability to communicate. Three-dimensional flow separation induced by a wall-mounted model polyp and its impact on the wall pressure loading are examined using particle image velocimetry, skin friction line visualization, and wall pressure measurements.

The fluid-structure energy exchange process for normal speech has been studied extensively, but it is not well understood for pathological conditions. ...

Hybrid &#181;CT-FMT imaging and image analysis

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to assess the biodistribution of novel probes and to assess disease progression using established molecular probes or reporter genes. The combination with an anatomical modality, e.g., micro computed tomography (&#181;CT), is beneficial for image analysis and for fluorescence reconstruction. We describe a protocol for multimodal &#181;CT-FMT imaging including the image processing steps necessary to extract quantitative measurements. After preparing the mice and performing the imaging, the multimodal data sets are registered. Subsequently, an improved fluorescence reconstruction is performed, which takes into account the shape of the mouse. For quantitative analysis, organ segmentations are generated based on the anatomical data using our interactive segmentation tool. Finally, the biodistribution curves are generated using a batch-processing feature. We show the applicability of the method by assessing the biodistribution of a well-known probe that binds to bones and joints.

Hybrid  &#181;CT-FMT imaging and image analysis

We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (&#181;CT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to ...

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Nanomaterials are increasingly prevalent throughout industry, manufacturing, and biomedical research. The need for tools and techniques that aid in the identification, localization, and characterization of nanoscale materials in biological samples is on the rise. Currently available methods, such as electron microscopy, tend to be resource-intensive, making their use prohibitive for much of the research community. Enhanced darkfield microscopy complemented with a hyperspectral imaging system may provide a solution to this bottleneck by enabling rapid and less expensive characterization of nanoparticles in histological samples. This method allows for high-contrast nanoscale imaging as well as nanomaterial identification. For this technique, histological tissue samples are prepared as they would be for light-based microscopy. First, positive control samples are analyzed to generate the reference spectra that will enable the detection of a material of interest in the sample. Negative controls without the material of interest are also analyzed in order to improve specificity (reduce false positives). Samples can then be imaged and analyzed using methods and software for hyperspectral microscopy or matched against these reference spectra in order to provide maps of the location of materials of interest in a sample. The technique is particularly well-suited for materials with highly unique reflectance spectra, such as noble metals, but is also applicable to other materials, such as semi-metallic oxides. This technique provides information that is difficult to acquire from histological samples without the use of electron microscopy techniques, which may provide higher sensitivity and resolution, but are vastly more resource-intensive and time-consuming than light microscopy.

Enhanced darkfield microscopy and hyperspectral imaging with spectral mapping enable screening, localization, and identification of nanoscale materials in histological samples with improved speed and accuracy over traditional methods. The goal of this paper is to provide methods for darkfield imaging and hyperspectral mapping of metal oxide nanoparticles in histological samples.

Nanomaterials are increasingly prevalent throughout industry, manufacturing, and biomedical research. The need for tools and techniques that aid in the ...

Alternating Magnetic Field-Responsive Hybrid Gelatin Microgels for Controlled Drug Release

Magnetically-responsive nano/micro-engineered biomaterials that enable a tightly controlled, on-demand drug delivery have been developed as new types of smart soft devices for biomedical applications. Although a number of magnetically-responsive drug delivery systems have demonstrated efficacies through either in vitro proof of concept studies or in vivo preclinical applications, their use in clinical settings is still limited by their insufficient biocompatibility or biodegradability. Additionally, many of the existing platforms rely on sophisticated techniques for their fabrications. We recently demonstrated the fabrication of biodegradable, gelatin-based thermo-responsive microgel by physically entrapping poly(N-isopropylacrylamide-co-acrylamide) chains as a minor component within a three-dimensional gelatin network. In this study, we present a facile method to fabricate a biodegradable drug release platform that enables a magneto-thermally triggered drug release. This was achieved by incorporating superparamagnetic iron oxide nanoparticles and thermo-responsive polymers within gelatin-based colloidal microgels, in conjunction with an alternating magnetic field application system.

We present a facile method to fabricate a biodegradable gelatin-based drug release platform that is magneto-thermally responsive. This was achieved by incorporating superparamagnetic iron oxide nanoparticles and poly(N-isopropylacrylamide-co-acrylamide) within a spherical gelatin micro-network crosslinked by genipin, in conjunction with an alternating magnetic field application system.

Magnetically-responsive nano/micro-engineered biomaterials that enable a tightly controlled, on-demand drug delivery have been developed as new types of ...

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Tissue expansion is a popular technique in plastic and reconstructive surgery that grows skin in vivo for correction of large defects such as burns and giant congenital nevi. Despite its widespread use, planning and executing an expansion protocol is challenging due to the difficulty in measuring the deformation imposed at each inflation step and over the length of the procedure. Quantifying the deformation fields is crucial, as the distribution of stretch over time determines the rate and amount of skin grown at the end of the treatment. In this manuscript, we present a method to study tissue expansion in order to gain quantitative knowledge of the deformations induced during an expansion process. This experimental protocol incorporates multi-view stereo and isogeometric kinematic analysis in a porcine model of tissue expansion. Multi-view stereo allows three-dimensional geometric reconstruction from uncalibrated sequences of images. The isogeometric kinematic analysis uses splines to describe the regional deformations between smooth surfaces with few mesh points. Our protocol has the potential to bridge the gap between basic scientific inquiry regarding the mechanics of skin expansion and the clinical setting. Eventually, we expect that the knowledge gained with our methodology will enable treatment planning using computational simulations of skin deformation in a personalized manner.

This protocol uses multi-view stereo to generate three-dimensional (3D) models out of uncalibrated sequences of photographs, making it affordable and adjustable to a surgical setting. Strain maps between the 3D models are quantified with spline-based isogeometric kinematics, which facilitate representation of smooth surfaces over coarse meshes sharing the same parameterization.

Tissue expansion is a popular technique in plastic and reconstructive surgery that grows skin in vivo for correction of large defects such as burns and ...

Bioluminescence Imaging to Detect Late Stage Infection of African Trypanosomiasis

Human African trypanosomiasis (HAT) is a multi-stage disease that manifests in two stages; an early blood stage and a late stage when the parasite invades the central nervous system (CNS). In vivo study of the late stage has been limited as traditional methodologies require the removal of the brain to determine the presence of the parasites.
Bioluminescence imaging is a non-invasive, highly sensitive form of optical imaging that enables the visualization of a luciferase-transfected pathogen in real-time. By using a transfected trypanosome strain that has the ability to produce late stage disease in mice we are able to study the kinetics of a CNS infection in a single animal throughout the course of infection, as well as observe the movement and dissemination of a systemic infection.
Here we describe a robust protocol to study CNS infections using a bioluminescence model of African trypanosomiasis, providing real time non-invasive observations which can be further analyzed with optional downstream approaches.

This manuscript describes the use of a bioluminescent strain of African trypanosomes to enable the tracking of late stage infection and demonstrates how in vivo live imaging can be used to visualize infections within the central nervous system in real-time.

Human African trypanosomiasis (HAT) is a multi-stage disease that manifests in two stages; an early blood stage and a late stage when the parasite invades ...

Immunology and Infection

Detection of Trypanosoma brucei Variant Surface Glycoprotein Switching by Magnetic Activated Cell Sorting and Flow Cytometry

Trypanosoma brucei, a protozoan parasite that causes both Human and Animal African Trypanosomiasis (known as sleeping sickness and nagana, respectively) cycles between a tsetse vector and a mammalian host. It evades the mammalian host immune system by periodically switching the dense, variant surface glycoprotein (VSG) that covers its surface. The detection of antigenic variation in Trypanosoma brucei can be both cumbersome and labor intensive. Here, we present a method for quantifying the number of parasites that have 'switched' to express a new VSG in a given population. The parasites are first stained with an antibody against the starting VSG, and then stained with a secondary antibody attached to a magnetic bead. Parasites expressing the starting VSG are then separated from the rest of the population by running the parasites over a column attached to a magnet. Parasites expressing the dominant, starting VSG are retained on the column, while the flow-through contains parasites that express a new VSG as well as some contaminants expressing the starting VSG. This flow-through population is stained again with a fluorescently labeled antibody against the starting VSG to label contaminants, and propidium iodide (PI), which labels dead cells. A known number of absolute counting beads that are visible by flow cytometry are added to the flow-through population. The ratio of beads to number of cells collected can then be used to extrapolate the number of cells in the entire sample. Flow cytometry is used to quantify the population of switchers by counting the number of PI negative cells that do not stain positively for the starting, dominant VSG. The proportion of switchers in the population can then be calculated using the flow cytometry data.

Detection of Trypanosoma brucei Variant Surface Glycoprotein Switching by Magnetic Activated Cell Sorting and Flow Cytometry

African trypanosomes grown in vitro undergo antigenic variation at a low rate, such that populations are made up of parasites expressing a dominant variant surface glycoprotein (VSG) type and a small population of "switched" variants. This protocol describes a fast method for detecting and quantifying these populations.

Trypanosoma brucei, a protozoan parasite that causes both Human and Animal African Trypanosomiasis (known as sleeping sickness and nagana, respectively) ...

Detection of Endotoxin in Nano-formulations Using Limulus Amoebocyte Lysate (LAL) Assays

When present in pharmaceutical products, a Gram-negative bacterial cell wall component endotoxin (often also called lipopolysaccharide) can cause inflammation, fever, hypo- or hypertension, and, in extreme cases, can lead to tissue and organ damage that may become fatal. The amounts of endotoxin in pharmaceutical products, therefore, are strictly regulated. Among the methods available for endotoxin detection and quantification, the Limulus Amoebocyte Lysate (LAL) assay is commonly used worldwide. While any pharmaceutical product can interfere with the LAL assay, nano-formulations represent a particular challenge due to their complexity. The purpose of this paper is to provide a practical guide to researchers inexperienced in estimating endotoxins in engineered nanomaterials and nanoparticle-formulated drugs. Herein, practical recommendations for performing three LAL formats including turbidity, chromogenic and gel-clot assays are discussed. These assays can be used to determine endotoxin contamination in nanotechnology-based drug products, vaccines, and adjuvants.

Detection of Endotoxin in Nano-formulations Using Limulus Amoebocyte Lysate LAL Assays

Detection of endotoxins in engineered nanomaterials represents one of the grand challenges in the field of nanomedicine. Here, we present a case study that describes the framework composed of three different LAL formats to estimate potential endotoxin contamination in nanoparticles.

When present in pharmaceutical products, a Gram-negative bacterial cell wall component endotoxin (often also called lipopolysaccharide) can cause ...

Detection of Extravascular Trypanosoma Parasites by Fine Needle Aspiration

Fine Needle Aspiration (FNA) is a routine diagnostic procedure essential to both medical and veterinary practices. It consists of the percutaneous aspiration of cells and/or microorganisms from palpable masses, organs or effusions (fluid accumulation in a body cavity) using a thin needle similar to the regular needle used for the venous puncture. The material collected by FNA is in general highly cellular, and the retrieved aspirate is then smeared, air dried, wet-fixed, stained and observed under a microscope. In the clinical context, FNA is an important diagnostic tool that serves as a guide to the appropriate therapeutic management. Because it is simple, fast, minimally invasive and requires limited investment in the laboratory and human resources, it is extensively used by veterinary practitioners, mostly in domestic, but also in farm animals. In studies using animal models, FNA has the advantage that it can be performed repeatedly in the same animal, enabling longitudinal studies through the collection of cells from tumors and organs/tissues over the course of the disease. In addition to routine microscopy, retrieved material can also be used for immunocytochemistry, electron microscopy, biochemical analysis, flow cytometry, molecular biology or in vitro assays. FNA has been used to identify the protozoan parasite Trypanosoma brucei in the gonads of infected mice, opening the possibility for a future diagnosis in cattle.

Detection of Extravascular Trypanosoma Parasites by Fine Needle Aspiration

Fine Needle Aspiration is a technique, whereby cells are obtained from a lesion or organ using a thin needle. Aspirated material is smeared, stained and examined under a microscope for diagnosis or used for molecular biology, cytometry or in vitro analysis. It is cheap, simple, quick and causes minimal trauma.

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

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