Compact off-axis holographic slide microscope: design guidelines

In-situ detection of microplastics in the aquatic environment: A systematic literature review

Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the ecosystem. Current laboratory-based microplastic detection methods suffer from various drawbacks, including a lack of standardisation, limited spatial and temporal coverage, high costs, and time-consuming procedures. Consequently, there is a need for the development of in-situ techniques to detect and monitor microplastics to effectively identify and understand their sources, pathways, and behaviours. Herein, we adopt a systematic literature review method to assess the development and application of experimental and field technologies designed for the in-situ detection and monitoring of aquatic microplastics, without the need for sample preparation. Four scientific databases were searched in March 2023, resulting in a review of 62 relevant studies. These studies were classified into seven sensor categories and their working principles were discussed. The sensor classes include optical devices, digital holography, Raman spectroscopy, other spectroscopy, hyperspectral imaging, remote sensing, and other methods. We also looked at how data from these technologies are integrated with machine learning models to develop classifiers capable of accurately characterising the physical and chemical properties of microplastics and discriminating them from other particles. This review concluded that in-situ detection of microplastics in aquatic environments is feasible and can be achieved with high accuracy, even though the methods are still in the early stages of development. Nonetheless, further research is still needed to enhance the in-situ detection of microplastics. This includes exploring the possibility of combining various detection methods and developing robust machine-learning classifiers. Additionally, there is a recommendation for in-situ implementation of the reviewed methods to assess their effectiveness in detecting microplastics and identify their limitations.

Complete holography-based system for the identification of microparticles in water samples

Here, we present a comprehensive holography-based system designed for detecting microparticles through microscopic holographic projections of water samples. This system is designed for researchers who may be unfamiliar with holographic technology but are engaged in microparticle research, particularly in the field of water analysis. Additionally, our innovative system can be deployed for environmental monitoring as a component of an autonomous sailboat robot. Our system's primary application is for large-scale classification of diverse microplastics that are prevalent in water bodies worldwide. This paper provides a step-by-step guide for constructing our system and outlines its entire processing pipeline, including hologram acquisition for image reconstruction.

Digital holographic approaches to the detection and characterization of microplastics in water environments

Microplastic (MP) pollution is seriously threatening the environmental health of the world, which has accelerated the development of new identification and characterization methods. Digital holography (DH) is one of the emerging tools to detect MPs in a high-throughput flow. Here, we review advances in MP screening by DH. We examine the problem from both the hardware and software viewpoints. Automatic analysis based on smart DH processing is reported by highlighting the role played by artificial intelligence for classification and regression tasks. In this framework, the continuous development and availability in recent years of field-portable holographic flow cytometers for water monitoring also is discussed.

AI-based analysis of 3D position and orientation of red blood cells using a digital in-line holographic microscopy

The morphological and mechanical characteristics of red blood cells (RBCs) largely vary depending on the occurrence of hematologic disorders. Variations in the rheological properties of RBCs affect the dynamic motions of RBCs, especially their rotational behavior. However, conventional techniques for measuring the orientation of biconcave-shaped RBCs still have some technical limitations, including complicated optical setups, complex post data processing, and low throughput. In this study, we propose a novel image-based technique for measuring 3D position and orientation of normal RBCs using digital in-line holographic microscopy (DIHM) and artificial intelligence (AI). Formaldehyde-fixed RBCs are immobilized in coagulated polydimethylsiloxane (PDMS). Holographic images of RBCs positioned at various out-of-plane angles are acquired by precisely manipulating the PDMS-trapped RBC sample attached to a 4-axis optical stage. With the aid of deep learning algorithms for data augmentation and regression analysis, the out-of-plane angle of RBCs is directly predicted from the captured holographic images. The 3D position and in-plane angle of RBCs are acquired by employing numerical reconstruction and ellipse detection methods. Combining these digital image processing techniques, the 3D positional and orientational information of each RBC recorded in a single holographic image is measured within 23.5 and 3.07 s, respectively. The proposed AI-based DIHM technique that can extract the 3D position, orientation, and morphology of individual RBCs would be utilized to analyze the dynamic translational and rotational motions of abnormal RBCs with hematologic disorders in shear flows through further research.

Ecotoxicity of Polyvinylidene Difluoride (PVDF) and Polylactic Acid (PLA) Microplastics in Marine Zooplankton

The aim of this study was to investigate the ecotoxicity of polyvinylidene difluoride (PVDF) and polylactic acid (PLA) microplastics (MPs) in two marine zooplankton: the crustacean Artemia franciscana and the cnidarian Aurelia sp. (common jellyfish). To achieve this goal, (i) MP uptake, (ii) immobility, and (iii) behavior (swimming speed, pulsation mode) of crustacean larval stages and jellyfish ephyrae exposed to MPs concentrations (1, 10, 100 mg/L) were assessed for 24 h. Using traditional and novel techniques, i.e., epifluorescence microscopy and 3D holotomography (HT), PVDF and PLA MPs were found in the digestive systems of the crustaceans and in the gelatinous tissue of jellyfish. Immobility was not affected in either organism, while a significant behavioral alteration in terms of pulsation mode was found in jellyfish after exposure to both PVDF and PLA MPs. Moreover, PLA MPs exposure in jellyfish induced a toxic effect (EC50: 77.43 mg/L) on the behavioral response. This study provides new insights into PLA and PVDF toxicity with the potential for a large impact on the marine ecosystem, since jellyfish play a key role in the marine food chain. However, further investigations incorporating additional species belonging to other trophic levels are paramount to better understand and clarify the impact of such polymers at micro scale in the marine environment. These findings suggest that although PVDF and PLA have been recently proposed as innovative and, in the case of PLA, biodegradable polymers, their effects on marine biota should not be underestimated.

Intelligent polarization-sensitive holographic flow-cytometer: Towards specificity in classifying natural and microplastic fibers

Micron size fiber fragments (MFFs), both natural and synthetic, are ubiquitous in our life, especially in textile clothes, being necessary in modern society. In the Earth's aquatic ecosystem, microplastic fibers account for ~91% of microplastic pollution, thus deserving notable attention as one of the most alarming ecological problems. Accurate automatic identification of MFFs discharges in specific upstream locations is highly demanded. Computational microscopy based on Digital Holography (DH) and machine learning has been demonstrated to identify microplastics in respect to microalgae. However, DH is a non-specific optical tool, meaning it cannot distinguish different types of plastic materials. On the other hand, materials-specific assessments are pivotal to establish the environmental impact of different textile products and production processes. Spectroscopic assays can be employed to identify microplastics for their intrinsic specificity, although they are generally low-throughput and require large concentrations to enable effective measurements. Conversely, MFFs are usually finely dispersed within a water sample. Here we rely on a polarization-resolved holographic flow cytometer in a Lab-on-Chip (LoC) platform for analysing MFFs. We demonstrate that two important objectives can be achieved, i.e. adding material specificity through polarization analysis while operating in a microfluidic stream modality. Through a machine learning numerical pipeline, natural fibers (i.e. cotton and wool) can be clearly separated from synthetic microfilaments, namely PA6, PA6.6, PET, PP. Moreover, the proposed system can accurately distinguish between different polymers under investigation, thus fulfilling the specificity goal. We extract and select different features from amplitude, phase and birefringence maps retrieved from the digital holograms. These are shown to typify MFFs without the need for sample pre-treatment or large concentrations. The simplicity of the DH method for identifying MFFs in LoC-based flow cytometers could promote the use of polarization resolved field-portable analysis systems suitable for studying pollution caused by washing processes of synthetic textiles.

Examination of blood samples using deep learning and mobile microscopy

BACKGROUND

Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards.

RESULTS

Instance segmentation models such as Mask R-CNN, Mask Scoring R-CNN, D2Det and YOLACT were trained and optimised for the detection and classification of all blood cell types. These networks were not designed to detect very small objects in large numbers, so extensive modifications were necessary. Thus, segmentation of all blood cell types and their classification was feasible with great accuracy: qualitatively evaluated, mean average precision of 0.57 and mean average recall of 0.61 are achieved for all blood cell types. Quantitatively, 93% of ground truth blood cells can be detected.

CONCLUSIONS

Mobile blood testing as a point-of-care system can be performed with diagnostic accuracy using deep learning methods. In the future, this application could enable very fast, cheap, location- and knowledge-independent patient care.

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.

Inclusion of shape parameters increases the accuracy of 3D models for microplastics mass quantification

As microplastics may bring about adverse effects on living organisms, it is important to establish more precise quantification approaches to better understand their dynamics. One method to determine the concentration of microplastics is to estimate their mass using three-dimensional (3D) models, but its accuracy is not well known. In this study, we evaluated the shape of the particles and verified the accuracy of a 3D model-based mass estimation using samples from a tidal flat facing Tokyo Bay. The particle shape evaluation suggested that the microplastics were flat and irregular in shape; based on these data, we created two types of models to estimate their mass. As a result, an accuracy of mass estimation by our model was higher than other models that consider the slenderness and flatness of particles. The optimization of mass estimation methods based on 3D models may improve the reliability of microplastic evaluation in monitoring studies.

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.

PhaseRMiC: phase real-time microscope camera for live cell imaging

We design a novel phase real-time microscope camera (PhaseRMiC) for live cell phase imaging. PhaseRMiC has a simple and cost-effective configuration only consisting of a beam splitter and a board-level camera with two CMOS imaging chips. Moreover, integrated with 3-D printed structures, PhaseRMiC has a compact size of 136×91×60 mm³, comparable to many commercial microscope cameras, and can be directly connected to the microscope side port. Additionally, PhaseRMiC can be well adopted in real-time phase imaging proved with satisfied accuracy, good stability and large field of view. Considering its compact and cost-effective device design as well as real-time phase imaging capability, PhaseRMiC is a preferred solution for live cell imaging.

Heterodyne phase shifting method in scanning probe microscopy

The present paper describes a novel implementation of the continuous phase shifting method (PSM), named heterodyne holography, in a scanning probe microscope configuration, able to retrieve the complex scattered field in on-axis configuration. This can be achieved by acquiring a continuous sequence of holograms at different wavelengths in just a single scan through the combination of scanning interference microscopy and a low-coherent signal acquired in the frequency domain. This method exploits the main advantages of the phase shifting technique and avoids some limits relative to off-axis holography in providing quantitative phase imaging.

Open-source, cost-effective, portable, 3D-printed digital lensless holographic microscope

In this work, the design, construction, and testing of the most cost-effective digital lensless holographic microscope to date are presented. The architecture of digital lensless holographic microscopy (DLHM) is built by means of a 3D-printed setup and utilizing off-the-shelf materials to produce a DLHM microscope costing US$52.82. For the processing of the recorded in-line holograms, an open-source software specifically developed to process this type of recordings is utilized. The presented DLHM setup has all the degrees of freedom needed to achieve different fields of view, levels of spatial resolution, and 2D scanning of the sample. The feasibility of the presented platform is tested by imaging non-bio and bio samples; the resolution test targets, a section of the head of a Drosophila melanogaster fly, red blood cells, and cheek cells are imaged on the built microscope.

Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy

Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition.

Deep learning-based cell identification and disease diagnosis using spatio-temporal cellular dynamics in compact digital holographic microscopy

We demonstrate a successful deep learning strategy for cell identification and disease diagnosis using spatio-temporal cell information recorded by a digital holographic microscopy system. Shearing digital holographic microscopy is employed using a low-cost, compact, field-portable and 3D-printed microscopy system to record video-rate data of live biological cells with nanometer sensitivity in terms of axial membrane fluctuations, then features are extracted from the reconstructed phase profiles of segmented cells at each time instance for classification. The time-varying data of each extracted feature is input into a recurrent bi-directional long short-term memory (Bi-LSTM) network which learns to classify cells based on their time-varying behavior. Our approach is presented for cell identification between the morphologically similar cases of cow and horse red blood cells. Furthermore, the proposed deep learning strategy is demonstrated as having improved performance over conventional machine learning approaches on a clinically relevant dataset of human red blood cells from healthy individuals and those with sickle cell disease. The results are presented at both the cell and patient levels. To the best of our knowledge, this is the first report of deep learning for spatio-temporal-based cell identification and disease detection using a digital holographic microscopy system.

Optical Technologies for Improving Healthcare in Low-Resource Settings: introduction to the feature issue

This feature issue of Biomedical Optics Express presents a cross-section of interesting and emerging work of relevance to optical technologies in low-resource settings. In particular, the technologies described here aim to address challenges to meeting healthcare needs in resource-constrained environments, including in rural and underserved areas. This collection of 18 papers includes papers on both optical system design and image analysis, with applications demonstrated for ex vivo and in vivo use. All together, these works portray the importance of global health research to the scientific community and the role that optics can play in addressing some of the world's most pressing healthcare challenges.