Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning

Machine learning in point-of-care testing: innovations, challenges, and opportunities

The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective explores how ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, illustrating their impact through different applications. We also discuss several challenges, such as regulatory hurdles, reliability, and privacy concerns, that must be overcome for the widespread adoption of ML-enhanced POCT in clinical settings and provide a comprehensive overview of the current state of ML-driven POCT technologies, highlighting their potential impact in the future of healthcare.

Trajectory-driven computational analysis for element characterization in Trypanosoma cruzi video microscopy

Optical microscopy videos enable experts to analyze the motion of several biological elements. Particularly in blood samples infected with Trypanosoma cruzi (T. cruzi), microscopy videos reveal a dynamic scenario where the parasites' motions are conspicuous. While parasites have self-motion, cells are inert and may assume some displacement under dynamic events, such as fluids and microscope focus adjustments. This paper analyzes the trajectory of T. cruzi and blood cells to discriminate between these elements by identifying the following motion patterns: collateral, fluctuating, and pan-tilt-zoom (PTZ). We consider two approaches: i) classification experiments for discrimination between parasites and cells; and ii) clustering experiments to identify the cell motion. We propose the trajectory step dispersion (TSD) descriptor based on standard deviation to characterize these elements, outperforming state-of-the-art descriptors. Our results confirm motion is valuable in discriminating T. cruzi of the cells. Since the parasites perform the collateral motion, their trajectory steps tend to randomness. The cells may assume fluctuating motion following a homogeneous and directional path or PTZ motion with trajectory steps in a restricted area. Thus, our findings may contribute to developing new computational tools focused on trajectory analysis, which can advance the study and medical diagnosis of Chagas disease.

All-optical dual module platform for motility-based functional scrutiny of microencapsulated probiotic bacteria

Probiotic bacteria are widely used in pharmaceutics to offer health benefits. Microencapsulation is used to deliver probiotics into the human body. Capsules in the stomach have to keep bacteria constrained until release occurs in the intestine. Once outside, bacteria must maintain enough motility to reach the intestine walls. Here, we develop a platform based on two label-free optical modules for rapidly screening and ranking probiotic candidates in the laboratory. Bio-speckle dynamics assay tests the microencapsulation effectiveness by simulating the gastrointestinal transit. Then, a digital holographic microscope 3D-tracks their motility profiles at a single element level to rank the strains.

From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film

Artificial intelligence (AI) and its application in classification of blood cells in the peripheral blood film is an evolving field in haematology. We performed a rapid review of the literature on AI and peripheral blood films, evaluating the condition studied, image datasets, machine learning models, training set size, testing set size and accuracy. A total of 283 studies were identified, encompassing 6 broad domains: malaria (n = 95), leukemia (n = 81), leukocytes (n = 72), mixed (n = 25), erythrocytes (n = 15) or Myelodysplastic syndrome (MDS) (n = 1). These publications have demonstrated high self-reported mean accuracy rates across various studies (95.5% for malaria, 96.0% for leukemia, 94.4% for leukocytes, 95.2% for mixed studies and 91.2% for erythrocytes), with an overall mean accuracy of 95.1%. Despite the high accuracy, the challenges toward real world translational usage of these AI trained models include the need for well-validated multicentre data, data standardisation, and studies on less common cell types and non-malarial blood-borne parasites.

Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning

A plaque assay-the gold-standard method for measuring the concentration of replication-competent lytic virions-requires staining and usually more than 48 h of runtime. Here we show that lens-free holographic imaging and deep learning can be combined to expedite and automate the assay. The compact imaging device captures phase information label-free at a rate of approximately 0.32 gigapixels per hour per well, covers an area of about 30 × 30 mm2 and a 10-fold larger dynamic range of virus concentration than standard assays, and quantifies the infected area and the number of plaque-forming units. For the vesicular stomatitis virus, the automated plaque assay detected the first cell-lysing events caused by viral replication as early as 5 h after incubation, and in less than 20 h it detected plaque-forming units at rates higher than 90% at 100% specificity. Furthermore, it reduced the incubation time of the herpes simplex virus type 1 by about 48 h and that of the encephalomyocarditis virus by about 20 h. The stain-free assay should be amenable for use in virology research, vaccine development and clinical diagnosis.

Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)

Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.

Deep tech innovation for parasite diagnosis: New dimensions and opportunities

By converging advanced science, engineering, and design, deep techs are bringing a great wave of future innovations by mastering challenges and problem complexity across sectors and the field of parasitology is no exception. Remarkable research and advancements can be seen in the field of parasite detection and diagnosis through smartphone applications. Supervised and unsupervised data deep learnings are heavily exploited for the development of automated neural network models for the prediction of parasites, eggs, etc., From microscopic smears and/or sample images with more than 99% accuracy. It is expected that several models will emerge in the future wherein greater attention is being paid to improving the model's accuracy. Invariably, it will increase the chances of adoption across the commercial sectors dealing in health and related applications. However, parasitic life cycle complexity, host range, morphological forms, etc., need to be considered further while developing such models to make the deep tech innovations perfect for bedside and field applications. In this review, the recent development of deep tech innovations focusing on human parasites has been discussed focusing on the present and future dimensions, opportunities, and applications.

Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry

Quantitative Phase Imaging (QPI) has gained popularity in bioimaging because it can avoid the need for cell staining, which in some cases is difficult or impossible. However, as a result, QPI does not provide labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for QPI techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed through the tomographic phase microscopy in flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy (FM) data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting specific three-dimensional intracellular structures directly from the phase-contrast data in a typical flow cytometry configuration.

Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears

Diagnosis of malaria in endemic areas is hampered by the lack of a rapid, stain-free and sensitive method to directly identify parasites in peripheral blood. Herein, we report the use of Fourier ptychography to generate wide-field high-resolution quantitative phase images of erythrocytes infected with malaria parasites, from a whole blood sample. We are able to image thousands of erythrocytes (red blood cells) in a single field of view and make a determination of infection status of the quantitative phase image of each segmented cell based on machine learning (random forest) and deep learning (VGG16) models. Our random forest model makes use of morphology and texture based features of the quantitative phase images. In order to label the quantitative images of the cells as either infected or uninfected before training the models, we make use of a Plasmodium berghei strain expressing GFP (green fluorescent protein) in all life cycle stages. By overlaying the fluorescence image with the quantitative phase image we could identify the infected subpopulation of erythrocytes for labelling purposes. Our machine learning model (random forest) achieved 91% specificity and 72% sensitivity while our deep learning model (VGG16) achieved 98% specificity and 57% sensitivity. These results highlight the potential for quantitative phase imaging coupled with artificial intelligence to develop an easy to use platform for the rapid and sensitive diagnosis of malaria.

cGAN-assisted imaging through stationary scattering media

Analyzing images taken through scattering media is challenging, owing to speckle decorrelations from perturbations in the media. For in-line imaging modalities, which are appealing because they are compact, require no moving parts, and are robust, negating the effects of such scattering becomes particularly challenging. Here we explore the use of conditional generative adversarial networks (cGANs) to mitigate the effects of the additional scatterers in in-line geometries, including digital holographic microscopy. Using light scattering simulations and experiments on objects of interest with and without additional scatterers, we find that cGANs can be quickly trained with minuscule datasets and can also efficiently learn the one-to-one statistical mapping between the cross-domain input-output image pairs. Importantly, the output images are faithful enough to enable quantitative feature extraction. We also show that with rapid training using only 20 image pairs, it is possible to negate this undesired scattering to accurately localize diffraction-limited impulses with high spatial accuracy, therefore transforming a shift variant system to a linear shift invariant (LSI) system.

Resolution and Contrast Enhancement for Lensless Digital Holographic Microscopy and Its Application in Biomedicine

An important imaging technique in biomedicine, the conventional optical microscopy relies on relatively complicated and bulky lens and alignment mechanics. Based on the Gabor holography, the lensless digital holographic microscopy has the advantages of light weight and low cost. It has developed rapidly and received attention in many fields. However, the finite pixel size at the sensor plane limits the spatial resolution. In this study, we first review the principle of lensless digital holography, then go over some methods to improve image contrast and discuss the methods to enhance the image resolution of the lensless holographic image. Moreover, the applications of lensless digital holographic microscopy in biomedicine are reviewed. Finally, we look forward to the future development and prospect of lensless digital holographic technology.

Deep learning-based image processing in optical microscopy

Optical microscopy has emerged as a key driver of fundamental research since it provides the ability to probe into imperceptible structures in the biomedical world. For the detailed investigation of samples, a high-resolution image with enhanced contrast and minimal damage is preferred. To achieve this, an automated image analysis method is preferable over manual analysis in terms of both speed of acquisition and reduced error accumulation. In this regard, deep learning (DL)-based image processing can be highly beneficial. The review summarises and critiques the use of DL in image processing for the data collected using various optical microscopic techniques. In tandem with optical microscopy, DL has already found applications in various problems related to image classification and segmentation. It has also performed well in enhancing image resolution in smartphone-based microscopy, which in turn enablse crucial medical assistance in remote places.

Graphical abstract

Deep learning for microscopic examination of protozoan parasites

The infectious and parasitic diseases represent a major threat to public health and are among the main causes of morbidity and mortality. The complex and divergent life cycles of parasites present major difficulties associated with the diagnosis of these organisms by microscopic examination. Deep learning has shown extraordinary performance in biomedical image analysis including various parasites diagnosis in the past few years. Here we summarize advances of deep learning in the field of protozoan parasites microscopic examination, focusing on publicly available microscopic image datasets of protozoan parasites. In the end, we summarize the challenges and future trends, which deep learning faces in protozoan parasite diagnosis.

Kinematic analysis and visualization of Tetraselmis microalgae 3D motility by digital holography

A study on locomotion in a 3D environment of Tetraselmis microalgae by digital holographic microscopy is reported. In particular, a fast and semiautomatic criterion is revealed for tracking and analyzing the swimming path of a microalga (i.e., Tetraselmis species) in a 3D volume. Digital holography (DH) in a microscope off-axis configuration is exploited as a useful method to enable fast autofocusing and recognition of objects in the field of view, thus coupling DH with appropriate numerical algorithms. Through the proposed method we measure, simultaneously, the tri-dimensional paths followed by the flagellate microorganism and the full set of the kinematic parameters that describe the swimming behavior of the analyzed microorganisms by means of a polynomial fitting and segmentation. Furthermore, the method is capable to furnish the accurate morphology of the microorganisms at any instant of time along its 3D trajectory. This work launches a promising trend having as the main objective the combined use of DH and motility microorganism analysis as a label-free and non-invasive environmental monitoring tool, employable also for in situ measurements. Finally, we show that the locomotion can be visualized intriguingly by different modalities to furnish marine biologists with a clear 3D representation of all the parameters of the kinematic set in order to better understand the behavior of the microorganism under investigation.

Deep learning for digital holography: a review

Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities for DH. Here, we survey recent developments in various DH applications powered by deep learning algorithms. This article starts with a brief introduction to digital holographic imaging, then summarizes the most relevant deep learning techniques for DH, with discussions on their benefits and challenges. We then present case studies covering a wide range of problems and applications in order to highlight research achievements to date. We provide an outlook of several promising directions to widen the use of deep learning in various DH applications.

Biospeckle Analysis and Biofilm Electrostatic Tests, Two Useful Methods in Microbiology

The development of more sensitive methodologies, capable of quickly detecting and monitoring a microbial population present in a specific biological matrix, as well as performing to allow for the study of all its metabolic changes (e.g., during the formation of biofilm) to occur, is an essential requirement for both well-being and the food industry. Two techniques, in particular, have gained the attention of scientists: The first is “biospeckle”, an optical technique representing an innovative tool for applications in food quality, food safety, and nutraceuticals. With this technique, we can quickly evaluate and monitor the presence of bacteria (or their proliferation) in a solid or liquid biological matrix. In addition, the technique is helpful in quantifying and optimizing the correct storage time of the pro-biotics, if they are entrapped in matrices such as alginate and follow their survival rate in simulated gastro-intestinal conditions. A second technique with great chances is the “biofilm electrostatic test” (BET). BET undoubtedly represents a fast, simple, and highly reproducible tool suitable for admitting the evaluation of the in vitro bacterial capacity in order to adhere through an electrostatic interaction with a pyro-electrified carrier after only 2 h of incubation. BET could represent the way for a quick and standardized evaluation of bacterial resistance among biofilm-producing microorganisms through a fast evaluation of the potential presence of the biofilm.

Dehydration of plant cells shoves nuclei rotation allowing for 3D phase-contrast tomography

Single-cell phase-contrast tomography promises to become decisive for studying 3D intracellular structures in biology. It involves probing cells with light at wide angles, which unfortunately requires complex systems. Here we show an intriguing concept based on an inherent natural process for plants biology, i.e., dehydration, allowing us to easily obtain 3D-tomography of onion-epidermal cells' nuclei. In fact, the loss of water reduces the turgor pressure and we recognize it induces significant rotation of cells' nuclei. Thanks to the holographic focusing flexibility and an ad-hoc angles' tracking algorithm, we combine different phase-contrast views of the nuclei to retrieve their 3D refractive index distribution. Nucleolus identification capability and a strategy for measuring morphology, dry mass, biovolume, and refractive index statistics are reported and discussed. This new concept could revolutionize the investigation in plant biology by enabling dynamic 3D quantitative and label-free analysis at sub-nuclear level using a conventional holographic setup.

High-speed, three-dimensional imaging reveals chemotactic behaviour specific to human-infective Leishmania parasites

Cellular motility is an ancient eukaryotic trait, ubiquitous across phyla with roles in predator avoidance, resource access, and competition. Flagellar motility is seen in various parasitic protozoans, and morphological changes in flagella during the parasite life cycle have been observed. We studied the impact of these changes on motility across life cycle stages, and how such changes might serve to facilitate human infection. We used holographic microscopy to image swimming cells of different Leishmania mexicana life cycle stages in three dimensions. We find that the human-infective (metacyclic promastigote) forms display ‘run and tumble’ behaviour in the absence of stimulus, reminiscent of bacterial motion, and that they specifically modify swimming direction and speed to target host immune cells in response to a macrophage-derived stimulus. Non-infective (procyclic promastigote) cells swim more slowly, along meandering helical paths. These findings demonstrate adaptation of swimming phenotype and chemotaxis towards human cells.

Cellular lensing and near infrared fluorescent nanosensor arrays to enable chemical efflux cytometry

Nanosensors have proven to be powerful tools to monitor single cells, achieving spatiotemporal precision even at molecular level. However, there has not been way of extending this approach to statistically relevant numbers of living cells. Herein, we design and fabricate nanosensor array in microfluidics that addresses this limitation, creating a Nanosensor Chemical Cytometry (NCC). nIR fluorescent carbon nanotube array is integrated along microfluidic channel through which flowing cells is guided. We can utilize the flowing cell itself as highly informative Gaussian lenses projecting nIR profiles and extract rich information. This unique biophotonic waveguide allows for quantified cross-correlation of biomolecular information with various physical properties and creates label-free chemical cytometer for cellular heterogeneity measurement. As an example, the NCC can profile the immune heterogeneities of human monocyte populations at attomolar sensitivity in completely non-destructive and real-time manner with rate of ~600 cells/hr, highest range demonstrated to date for state-of-the-art chemical cytometry.

Detection of circulating microfilariae in canine EDTA blood using lens-free technology: preliminary results

Dirofilaria immitis causes life-threatening heart disease in dogs, thus screening of dog populations is important. Lens-free technology (LFT) is a low-cost imaging technique based on light diffraction that allows computerized recognition of small objects in holographic images. We evaluated an algorithm capable of recognizing microfilariae in canine whole blood using the LFT. We examined 3 groups of 10 EDTA blood specimens, from dogs with microfilaremia (group A), healthy dogs (B), and dogs with hematologic modifications other than microfilaremia (C). The LFT analyzer photographed repeated series of 5 images of all samples. The algorithm declared a sample positive if a microfilaria was detected on ≥1, ≥2, or ≥3 of the 5 images of a series. Microfilariae were detected visually in the images in 9 of 10 cases in group A; no microfilariae were seen in the images from groups B and C. Of the 30 cases, there were 14, 4, and only 3 false-positives with the 1 of 5, 2 of 5, and 3 of 5 image cutoffs, respectively. There were no false-negatives, regardless of cutoff. LFT seems useful for detecting microfilaria and could have application in clinical pathology.

Decoding Optical Data with Machine Learning

Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML-based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science and ML.

Collateral motion saliency-based model for Trypanosoma cruzi detection in dye-free blood microscopy

The motion performed by some protozoa is a crucial visual stimulus in microscopy analysis, especially when they have almost imperceptible morphological characteristics. Microorganisms can be distinguished through the interactions of their locomotion with neighboring elements, as observed in some parasitological analysis of Trypanosoma cruzi. In dye-free blood microscopy, the low contrast of this parasite makes it difficult to detect them. Thus, the parasite's interaction with the neighborhood, such as collisions with blood cells and shocks during the escape of confinements in cell clumps, generates collateral motions that assist its detection. Assuming that the collateral motion of the parasite can be sufficiently noticeable to overcome the dynamic contexts of inspection, we propose a novel computational approach that is based on motion saliency. We estimate motion in microscopy videos using dense optical flow and we investigate vestiges in saliency maps that could characterize the collateral motion of parasites. Our biological-inspired method shows that the parasite's collateral motion is a relevant feature for T. cruzi detection. Therefore, our computational model is a promising aid in the research and medical diagnosis of Chagas disease.

Lensless, reflection-based dark-field microscopy (RDFM) on a CMOS chip

We present for the first time a lens-free, oblique illumination imaging platform for on-sensor dark- field microscopy and shadow-based 3D object measurements. It consists of an LED point source that illuminates a 5-megapixel, 1.4 µm pixel size, back-illuminated CMOS sensor at angles between 0° and 90°. Analytes (polystyrene beads, microorganisms, and cells) were placed and imaged directly onto the sensor. The spatial resolution of this imaging system is limited by the pixel size (∼1.4 µm) over the whole area of the sensor (3.6×2.73 mm). We demonstrated two imaging modalities: (i) shadow imaging for estimation of 3D object dimensions (on polystyrene beads and microorganisms) when the illumination angle is between 0° and 85°, and (ii) dark-field imaging, at >85° illumination angles. In dark-field mode, a 3-4 times drop in background intensity and contrast reversal similar to traditional dark-field imaging was observed, due to larger reflection intensities at those angles. With this modality, we were able to detect and analyze morphological features of bacteria and single-celled algae clusters.

Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning

Early identification of pathogenic bacteria in food, water, and bodily fluids is very important and yet challenging, owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time, accuracy/sensitivity, cost, and sample preparation complexity. Here, we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples, shortening the detection time by >12 h compared to the Environmental Protection Agency (EPA)-approved methods. Using the preincubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L in ≤9 h of total test time. This platform is highly cost-effective (~$0.6/test) and has high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.

Biospeckle-characterization of hairy root cultures using laser speckle photometry

Monitoring is indispensable for the optimization and simulation of biotechnological processes. Hairy roots (hr, plant tissue cultures) are producers of valuable relevant secondary metabolites. The genetically stable cultures are characterized by a rapid filamentous growth, making monitoring difficult with standard methods. This article focuses on the application of laser speckle photometry (LSP) as an innovative, non-invasive method to characterize Beta vulgaris (hr). LSP is based on the analysis of time-resolved interference patterns. Speckle interference patterns of a biological object, known as biospeckles, are characterized by a dynamic behavior that is induced by physical and biological phenomena related to the object. Speckle contrast, a means of measuring the dynamic behavior of biospeckles, was used to assess the biospeckle activity. The biospeckle activity corresponds to processes modifying the object and correlates with the biomass growth. Furthermore, the stage of the cultures' physiological development was assessed by speckle contrast due to the differentiation between active and low active behavior. This method is a new means of monitoring and evaluating the biomass growth of filamentous cultures in real time. As a potential tool to characterize hairy roots, LSP is non-invasive, time-saving, can be used online and stands out for its simple, low-cost setup.

Compact off-axis holographic slide microscope: design guidelines

Holographic microscopes are emerging as suitable tools for in situ diagnostics and environmental monitoring, providing high-throughput, label-free, quantitative imaging capabilities through small and compact devices. In-line holographic microscopes can be realized at contained costs, trading off complexity in the phase retrieval process and being limited to sparse samples. Here we present a 3D printed, cost effective and field portable off-axis holographic microscope based on the concept of holographic microfluidic slide. Our scheme removes complexity from the reconstruction process, as phase retrieval is non iterative and obtainable by hologram demodulation. The configuration we introduce ensures flexibility in the definition of the optical scheme, exploitable to realize modular devices with different features. We discuss trade-offs and design rules of thumb to follow for developing DH microscopes based on the proposed solution. Using our prototype, we image flowing marine microalgae, polystyrene beads, E.coli bacteria and microplastics. We detail the effect on the performance and costs of each parameter, design, and hardware choice, guiding readers toward the realization of optimized devices that can be employed out of the lab by non-expert users for point of care testing.

Dynamic biospeckle analysis, a new tool for the fast screening of plant nematicide selectivity

BACKGROUND

Plant feeding, free-living nematodes cause extensive damage to plant roots by direct feeding and, in the case of some trichodorid and longidorid species, through the transmission of viruses. Developing more environmentally friendly, target-specific nematicides is currently impeded by slow and laborious methods of toxicity testing. Here, we developed a bioactivity assay based on the dynamics of light 'speckle' generated by living cells and we demonstrate its application by assessing chemicals' toxicity to different nematode trophic groups.

RESULTS

Free-living nematode populations extracted from soil were exposed to methanol and phenyl isothiocyanate (PEITC). Biospeckle analysis revealed differing behavioural responses as a function of nematode feeding groups. Trichodorus nematodes were less sensitive than were bacterial feeding nematodes or non-trichodorid plant feeding nematodes. Following 24 h of exposure to PEITC, bioactivity significantly decreased for plant and bacterial feeders but not for Trichodorus nematodes. Decreases in movement for plant and bacterial feeders in the presence of PEITC also led to measurable changes to the morphology of biospeckle patterns.

CONCLUSIONS

Biospeckle analysis can be used to accelerate the screening of nematode bioactivity, thereby providing a fast way of testing the specificity of potential nematicidal compounds. With nematodes' distinctive movement and activity levels being visible in the biospeckle pattern, the technique has potential to screen the behavioural responses of diverse trophic nematode communities. The method discriminates both behavioural responses, morphological traits and activity levels and hence could be used to assess the specificity of nematicidal compounds.

The Use of Motion Analysis as Particle Biomarkers in Lensless Optofluidic Projection Imaging for Point of Care Urine Analysis

Urine testing is an essential clinical diagnostic tool. The presence of urine sediments, typically analyzed through microscopic urinalysis or cell culture, can be indicative of many diseases, including bacterial, parasitic, and yeast infections, as well as more serious conditions like bladder cancer. Current urine analysis diagnostic methods are usually centralized and limited by high cost, inconvenience, and poor sensitivity. Here, we developed a lensless projection imaging optofluidic platform with motion-based particle analysis to rapidly detect urinary constituents without the need for concentration or amplification through culture. A removable microfluidics channel ensures that urine samples do not cross contaminate and the lens-free projection video is captured and processed by a low-cost integrated microcomputer. A motion tracking and analysis algorithm is developed to identify and track moving objects in the flow. Their motion characteristics are used as biomarkers to detect different urine species in near real-time. The results show that this technology is capable of detection of red and white blood cells, Trichomonas vaginalis, crystals, casts, yeast and bacteria. This cost-effective device has the potential to be implemented for timely, point-of-care detection of a wide range of disorders in hospitals, clinics, long-term care homes, and in resource-limited regions.

Computational cytometer based on magnetically modulated coherent imaging and deep learning

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.

Long-term holographic phase-contrast time lapse reveals cytoplasmic circulation in dehydrating plant cells

The intracellular dynamics of onion epidermal cells during the dehydration process is observed by holographic microscopy. Both the nucleus and cytoplasm are accurately revealed by quantitative phase imaging while dehydration takes place. Indeed, we notice that the contrast of phase images increases with the decrease in cellular water content. We foresee that such a dehydrating process can be effective for improving phase contrast, thus permitting better imaging of plant cells with the scope of learning more about cellular dynamics and related phenomena. Exploiting this concept, we observe intracellular cytoplasmic circulation and transport of biological material.