Pocket MUSE: an affordable, versatile and high-performance fluorescence microscope using a smartphone

"Urinary tract infection: Conventional testing to developing Technologies"

Urinary tract infections (UTIs) present an escalating global health concern, precipitating increased hospitalizations and antibiotic utilization, thereby fostering the emergence of antimicrobial resistance. Current diagnostic modalities exhibit protracted timelines and substantial financial burdens, necessitating specialized infrastructures. Addressing these impediments mandates the development of a precise diagnostic paradigm to expedite identification and augment antibiotic stewardship. The application of biosensors, recognized for their transformative efficacy, emerges as a promising resolution. Recent strides in biosensor technologies have introduced pioneering methodologies, yielding pertinent biosensors and integrated systems with significant implications for point-of-care applications. This review delves into historical perspectives, furnishing a comprehensive delineation of advancements in UTI diagnostics, disease etiology, and biomarkers, underscoring the potential merits of these innovations for optimizing patient care.

Visualization of Gastric Adenocarcinoma Lymph Node Metastases by Microscopy with Ultraviolet Surface Excitation

The detection of lymph node metastases is crucial in oncopathology, as it makes it possible to determine the TNM stage, to design a treatment plan, and predict the survival for cancer patients. The current gold standard for this process is hematoxylin and eosin staining. However, new alternative methods leveraging the unique optical properties of tissue structures are being developed for rapid intraoperative or postoperative application. The aim of the study is to evaluate the effectiveness of identifying lymph node metastases using microscopy with ultraviolet surface excitation (MUSE).

Materials and Methods

17 lymph nodes from the Sechenov University archive (Russia) collected intraoperatively from 6 patients with gastric cancer have been investigated.In this study, we utilized a MUSE optical system consisting of three UV light-emitting diodes (265 nm) and the Axio Scope A1 microscope (Carl Zeiss, Germany) with various objectives. We introduced a novel combination of fluorescent dyes - Nile red and Hoechst - that had not been previously used with MUSE.

Results

The combination of fluorescent dyes yielded high-contrast images with blue-stained nuclei and orange-to-red stained cytoplasm, effectively visualizing gastric adenocarcinoma cells characterized by abundant cytoplasmic components and large polymorphic nuclei. The presence of irregularly shaped cavities, formed by adenocarcinoma metastases, was also detectable by MUSE.

Conclusion

Biophotonics provides alternative methods for tissue imaging. However, traditional methods are still unsurpassed in the accuracy of detecting cancer metastases and other pathologies. Further refinement of imaging protocols and expanded research into other cancer types are needed to make methods like MUSE applicable for intraoperative diagnosis.

Optical-resolution parallel ultraviolet photoacoustic microscopy for slide-free histology

Intraoperative imaging of slide-free specimens is crucial for oncology surgeries, allowing surgeons to quickly identify tumor margins for precise surgical guidance. While high-resolution ultraviolet photoacoustic microscopy has been demonstrated for slide-free histology, the imaging speed is insufficient, due to the low laser repetition rate and the limited depth of field. To address these challenges, we present parallel ultraviolet photoacoustic microscopy (PUV-PAM) with simultaneous scanning of eight optical foci to acquire histology-like images of slide-free fresh specimens, improving the ultraviolet PAM imaging speed limited by low laser repetition rates. The PUV-PAM has achieved an imaging speed of 0.4 square millimeters per second (i.e., 4.2 minutes per square centimeter) at 1.3-micrometer resolution using a 50-kilohertz laser. In addition, we demonstrated the PUV-PAM with eight needle-shaped beams for an extended depth of field, allowing fast imaging of slide-free tissues with irregular surfaces. We believe that the PUV-PAM approach will enable rapid intraoperative photoacoustic histology and provide prospects for ultrafast optical-resolution PAM.

Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications

The need for faster and more accessible alternatives to laboratory microscopy is driving many innovations throughout the image and data acquisition chain in the biomedical field. Benchtop microscopes are bulky, lack communications capabilities, and require trained personnel for analysis. New technologies, such as compact 3D-printed devices integrated with the Internet of Things (IoT) for data sharing and cloud computing, as well as automated image processing using deep learning algorithms, can address these limitations and enhance the conventional imaging workflow. This review reports on recent advancements in microscope miniaturization, with a focus on emerging technologies such as photoacoustic microscopy and more established approaches like smartphone-based microscopy. The potential applications of IoT in microscopy are examined in detail. Furthermore, this review discusses the evolution of image processing in microscopy, transitioning from traditional to deep learning methods that facilitate image enhancement and data interpretation. Despite numerous advancements in the field, there is a noticeable lack of studies that holistically address the entire microscopy acquisition chain. This review aims to highlight the potential of IoT and artificial intelligence (AI) in combination with portable microscopy, emphasizing the importance of a comprehensive approach to the microscopy acquisition chain, from portability to image analysis.

Towards ultra-low-cost smartphone microscopy

The outbreak of COVID-19 exposed the inadequacy of our technical tools for home health surveillance, and recent studies have shown the potential of smartphones as a universal optical microscopic imaging platform for such applications. However, most of them use laboratory-grade optomechanical components and transmitted illuminations to ensure focus tuning capability and imaging quality, which keeps the cost of the equipment high. Here, we propose an ultra-low-cost solution for smartphone microscopy. To realize focus tunability, we designed a seesaw-like structure capable of converting large displacements on one side into small displacements on the other (reduced to ∼9.1%), which leverages the intrinsic flexibility of 3D printing materials. We achieved a focus-tuning accuracy of ∼5 𝜇m, which is 40 times higher than the machining accuracy of the 3D-printed lens holder itself. For microscopic imaging, we used an off-the-shelf smartphone camera lens as the objective and the built-in flashlight as the illumination. To compensate for the resulting image quality degradation, we developed a learning-based image enhancement method. We used the CycleGAN architecture to establish the mapping from smartphone microscope images to benchtop microscope images without pairing. We verified the imaging performance on different biomedical samples. Except for the smartphone, we kept the full costs of the device under 4 USD. We think these efforts to lower the costs of smartphone microscopes will benefit their applications in various scenarios, such as point-of-care testing, on-site diagnosis, and home health surveillance. RESEARCH HIGHLIGHTS: We propose a solution for ultra-low-cost smartphone microscopy. Utilizing the flexibility of 3D-printed material, we can achieve focusing accuracy of ∼5 𝜇m. Such a low-cost device will benefit point-of-care diagnosis and home health surveillance.

Current Landscape of Advanced Imaging Tools for Pathology Diagnostics

Histopathology relies on century-old workflows of formalin fixation, paraffin embedding, sectioning, and staining tissue specimens on glass slides. Despite being robust, this conventional process is slow, labor-intensive, and limited to providing two-dimensional views. Emerging technologies promise to enhance and accelerate histopathology. Slide-free microscopy allows rapid imaging of fresh, unsectioned specimens, overcoming slide preparation delays. Methods such as fluorescence confocal microscopy, multiphoton microscopy, along with more recent innovations including microscopy with UV surface excitation and fluorescence-imitating brightfield imaging can generate images resembling conventional histology directly from the surface of tissue specimens. Slide-free microscopy enable applications such as rapid intraoperative margin assessment and, with appropriate technology, three-dimensional histopathology. Multiomics profiling techniques, including imaging mass spectrometry and Raman spectroscopy, provide highly multiplexed molecular maps of tissues, although clinical translation remains challenging. Artificial intelligence is aiding the adoption of new imaging modalities via virtual staining, which converts methods such as slide-free microscopy into synthetic brightfield-like or even molecularly informed images. Although not yet commonplace, these emerging technologies collectively demonstrate the potential to modernize histopathology. Artificial intelligence-assisted workflows will ease the transition to new imaging modalities. With further validation, these advances may transform the century-old conventional histopathology pipeline to better serve 21st-century medicine. This review provides an overview of these enabling technology platforms and discusses their potential impact.

Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis

Hematologists evaluate alterations in blood cell enumeration and morphology to confirm peripheral blood smear findings through manual microscopic examination. However, routine peripheral blood smear analysis is both time-consuming and labor-intensive. Here, we propose using smartphone-based autofluorescence microscopy (Smart-AM) for imaging label-free blood smears at subcellular resolution with automatic hematological analysis. Smart-AM enables rapid and label-free visualization of morphological features of normal and abnormal blood cells (including leukocytes, erythrocytes, and thrombocytes). Moreover, assisted with deep-learning algorithms, this technique can automatically detect and classify different leukocytes with high accuracy, and transform the autofluorescence images into virtual Giemsa-stained images which show clear cellular features. The proposed technique is portable, cost-effective, and user-friendly, making it significant for broad point-of-care applications.

Development and prospective validation of an artificial intelligence-based smartphone app for rapid intraoperative pituitary adenoma identification

BACKGROUND

Intraoperative pathology consultation plays a crucial role in tumor surgery. The ability to accurately and rapidly distinguish tumor from normal tissue can greatly impact intraoperative surgical oncology management. However, this is dependent on the availability of a specialized pathologist for a reliable diagnosis. We developed and prospectively validated an artificial intelligence-based smartphone app capable of differentiating between pituitary adenoma and normal pituitary gland using stimulated Raman histology, almost instantly.

METHODS

The study consisted of three parts. After data collection (part 1) and development of a deep learning-based smartphone app (part 2), we conducted a prospective study that included 40 consecutive patients with 194 samples to evaluate the app in real-time in a surgical setting (part 3). The smartphone app's sensitivity, specificity, positive predictive value, and negative predictive value were evaluated by comparing the diagnosis rendered by the app to the ground-truth diagnosis set by a neuropathologist.

RESULTS

The app exhibits a sensitivity of 96.1% (95% CI: 89.9-99.0%), specificity of 92.7% (95% CI: 74-99.3%), positive predictive value of 98% (95% CI: 92.2-99.8%), and negative predictive value of 86.4% (95% CI: 66.2-96.8%). An external validation of the smartphone app on 40 different adenoma tumors and a total of 191 scanned SRH specimens from a public database shows a sensitivity of 93.7% (95% CI: 89.3-96.7%).

CONCLUSIONS

The app can be readily expanded and repurposed to work on different types of tumors and optical images. Rapid recognition of normal versus tumor tissue during surgery may contribute to improved intraoperative surgical management and oncologic outcomes. In addition to the accelerated pathological assessments during surgery, this platform can be of great benefit in community hospitals and developing countries, where immediate access to a specialized pathologist during surgery is limited.

Feasibility of images acquired using smartphone camera for marginal and internal fit of fixed dental prosthesis: comparison and correlation study

This study aimed to measure marginal and internal fit using images captured with both an optical microscope and a smartphone camera, comparing the fit measurement performance of these devices and analyzing their correlation. Working casts (with 10 posterior and 10 anterior teeth) created to fabricate fixed dental prostheses were used. These working casts were scanned using a desktop scanner (E1) to design an interim crown, and the designed interim crown was fabricated using a three-dimensional (3D) printer. Utilizing the silicone replica technique, the fabricated interim crown replicated the fit, which was then captured using both an optical microscope and a smartphone camera. The captured images were used to measure the marginal and internal fit according to the imaging device. Intraclass correlation coefficients (ICC) were used for reliability analysis according to the imaging device. Furthermore, the Wilcoxon signed-rank test was adopted for the comparative evaluation of the marginal and internal fit between the imaging devices (α = 0.05). The measurement results of the marginal and internal fit according to the optical microscope and smartphone camera did exhibit a significant difference (P < 0.05). The ICC between the two devices showed an "excellent" agreement of over 0.9 at all measurement points (P < 0.001). A smartphone camera could be used to obtain images for evaluating the marginal and internal fit.

Optical imaging for screening and early cancer diagnosis in low-resource settings

Low-cost optical imaging technologies have the potential to reduce inequalities in healthcare by improving the detection of pre-cancer or early cancer and enabling more effective and less invasive treatment. In this Review, we summarise technologies for in vivo widefield, multi-spectral, endoscopic, and high-resolution optical imaging that could offer affordable approaches to improve cancer screening and early detection at the point-of-care. Additionally, we discuss approaches to slide-free microscopy, including confocal imaging, lightsheet microscopy, and phase modulation techniques that can reduce the infrastructure and expertise needed for definitive cancer diagnosis. We also evaluate how machine learning-based algorithms can improve the accuracy and accessibility of optical imaging systems and provide real-time image analysis. To achieve the potential of optical technologies, developers must ensure that devices are easy to use; the optical technologies must be evaluated in multi-institutional, prospective clinical tests in the intended setting; and the barriers to commercial scale-up in under-resourced markets must be overcome. Therefore, test developers should view the production of simple and effective diagnostic tools that are accessible and affordable for all countries and settings as a central goal of their profession.

AQAFI: a bioanalytical method for automated KPIs quantification of fluorescent images of human leukocytes and micro-nano particles

Micro-nanoparticle and leukocyte imaging find significant applications in the areas of infectious disease diagnostics, cellular therapeutics, and biomanufacturing. Portable fluorescence microscopes have been developed for these measurements, however, quantitative assessment of the quality of images (micro-nanoparticles, and leukocytes) captured using these devices remains a challenge. Here, we present a novel method for automated quality assessment of fluorescent images (AQAFI) captured using smartphone fluorescence microscopes (SFM). AQAFI utilizes novel feature extraction methods to identify and measure multiple features of interest in leukocyte and micro-nanoparticle images. For validation of AQAFI, fluorescent particles of different diameters (8.3, 2, 1, 0.8 μm) were imaged using custom-designed SFM at a range of excitation voltages (3.8-4.5 V). Particle intensity, particle vicinity intensity, and image background noise were chosen as analytical parameters of interest and measured by the AQAFI algorithm. A control method was developed by manual calculation of these parameters using ImageJ which was subsequently used to validate the performance of the AQAFI method. For micro-nanoparticle images, correlation coefficients with R2 > 0.95 were obtained for each parameter of interest while comparing AQAFI vs. control (ImageJ). Subsequently, key performance indicators (KPIs) i.e., signal difference to noise ratio (SDNR) and contrast to noise ratio (CNR) were defined and calculated for these micro-nano particle images using both AQAFI and control methods. Finally, we tested the performance of the AQAFI method on the fluorescent images of human peripheral blood leukocytes captured using our custom SFM. Correlation coefficients of R2 = 0.99 were obtained for each parameter of interest (leukocyte intensity, vicinity intensity, background noise) calculated using AQAFI and control (ImageJ). A high correlation was also found between the CNR and SDNR values calculated using both methods. The developed AQAFI method thus presents an automated and precise way to quantify and assess the quality of fluorescent images (micro-nano particles and leukocytes) captured using portable SFMs. Similarly, this study finds broader applicability and can also be employed with benchtop microscopes for the quantitative assessment of their imaging performance.

Machine learning for image-based multi-omics analysis of leaf veins

Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture.

FluoroPi Device With SmartProbes: A Frugal Point-of-Care System for Fluorescent Detection of Bacteria From a Pre-Clinical Model of Microbial Keratitis

Purpose

Rapid and accurate diagnosis of microbial keratitis (MK) could greatly improve patient outcomes. Here, we present the development of a rapid, accessible multicolour fluorescence imaging device (FluoroPi) and evaluate its performance in combination with fluorescent optical reporters (SmartProbes) to distinguish bacterial Gram status. Furthermore, we show feasibility by imaging samples obtained by corneal scrape and minimally invasive corneal impression membrane (CIM) from ex vivo porcine corneal MK models.

Methods

FluoroPi was built using a Raspberry Pi single-board computer and camera, light-emitting-diodes (LEDs), and filters for white-light and fluorescent imaging, with excitation and detection of bacterial optical SmartProbes: Gram-negative, NBD-PMX (exmax 488 nm); Gram positive, Merocy-Van (exmax 590 nm). We evaluated FluoroPi with bacteria (Pseudomonas aeruginosa and Staphylococcus aureus) isolated from ex vivo porcine corneal models of MK by scrape (needle) and CIM with the SmartProbes.

Results

FluoroPi provides <1 µm resolution and was able to readily distinguish bacteria isolated from ex vivo models of MK from tissue debris when combined with SmartProbes, retrieved by both scrape and CIM. Single bacteria could be resolved within the field of view, with limits of detection demonstrated as 103 to 104 CFU/mL. Sample preparation prior to imaging was minimal (wash-free), and imaging and postprocessing with FluoroPi were straightforward, confirming ease of use.

Conclusions

FluoroPi coupled with SmartProbes provides effective, low-cost bacterial imaging, delineating Gram-negative and Gram-positive bacteria directly sampled from a preclinical model of MK.

Translational Relevance

This study provides a crucial stepping stone toward clinical translation of a rapid, minimally invasive diagnostic approach for MK.

CRISPR-Enhanced Hydrogel Microparticles for Multiplexed Detection of Nucleic Acids

CRISPR/Cas systems offer a powerful sensing mechanism to transduce sequence-specific information into amplified analytical signals. However, performing multiplexed CRISPR/Cas assays remains challenging and often requires complex approaches for multiplexed assays. Here, a hydrogel-based CRISPR/Cas12 system termed CLAMP (Cas-Loaded Annotated Micro-Particles) is described. The approach compartmentalizes the CRISPR/Cas reaction in spatially-encoded hydrogel microparticles (HMPs). Each HMP is identifiable by its face code and becomes fluorescent when target DNA is present. The assay is further streamlined by capturing HMPs inside a microfluidic device; the captured particles are then automatically recognized by a machine-learning algorithm. The CLAMP assay is fast, highly sensitive (attomolar detection limits with preamplification), and capable of multiplexing in a single-pot assay. As a proof-of-concept clinical application, CLAMP is applied to detect nucleic acid targets of human papillomavirus in cervical brushing samples.

A low-cost smartphone fluorescence microscope for research, life science education, and STEM outreach

Much of our understanding of cell and tissue development, structure, and function stems from fluorescence microscopy. The acquisition of colorful and glowing images engages and excites users ranging from seasoned microscopists to STEM students. Fluorescence microscopes range in cost from several thousand to several hundred thousand US dollars. Therefore, the use of fluorescence microscopy is typically limited to well-funded institutions and biotechnology companies, research core facilities, and medical laboratories, but is financially impractical at many universities and colleges, primary and secondary schools (K-12), and in science outreach settings. In this study, we developed and characterized components that when used in combination with a smartphone or tablet, perform fluorescence microscopy at a cost of less than $50 US dollars per unit. We re-purposed recreational LED flashlights and theater stage lighting filters to enable viewing of green and red fluorophores including EGFP, DsRed, mRFP, and mCherry on a simple-to-build frame made of wood and plexiglass. These devices, which we refer to as glowscopes, were capable of 10 µm resolution, imaging fluorescence in live specimens, and were compatible with all smartphone and tablet models we tested. In comparison to scientific-grade fluorescence microscopes, glowscopes may have limitations to sensitivity needed to detect dim fluorescence and the inability to resolve subcellular structures. We demonstrate capability of viewing fluorescence within zebrafish embryos, including heart rate, rhythmicity, and regional anatomy of the central nervous system. Due to the low cost of individual glowscope units, we anticipate this device can help to equip K-12, undergraduate, and science outreach classrooms with fleets of fluorescence microscopes that can engage students with hands-on learning activities.

Smartphone-based hand-held polarized light microscope for on-site pharmaceutical crystallinity characterization

Polarized light microscopy (PLM) is a common but critical method for pharmaceutical crystallinity characterization, which has been widely introduced for research purposes or drug testing and is recommended by many pharmacopeias around the world. To date, crystallinity characterization of pharmaceutical solids is restricted to laboratories due to the relatively bulky design of the conventional PLM system, while little attention has been paid to on-site, portable, and low-cost applications. Herein, we developed a smartphone-based polarized microscope with an ultra-miniaturization design ("hand-held" scale) for these purposes. The compact system consists of an optical lens, two polarizers, and a tailor-made platform to hold the smartphone. Analytical performance parameters including resolution, imaging quality of interference color, and imaging reproducibility were measured. In a first approach, we illustrated the suitability of the device for pharmaceutical crystallinity characterization and obtained high-quality birefringence images comparable to a conventional PLM system, and we also showed the great promise of the device for on-site characterization with high flexibility. In a second approach, we employed the device as a proof of concept for a wider application ranging from liquid crystal to environmental pollutants or tissues from plants. As such, this smartphone-based hand-held polarized light microscope shows great potential in helping pharmacists both for research purposes and on-site drug testing, not to mention its broad application prospects in many other fields.

Portable, Automated and Deep-Learning-Enabled Microscopy for Smartphone-Tethered Optical Platform Towards Remote Homecare Diagnostics: A Review

Globally new pandemic diseases induce urgent demands for portable diagnostic systems to prevent and control infectious diseases. Smartphone-based portable diagnostic devices are significantly efficient tools to user-friendly connect personalized health conditions and collect valuable optical information for rapid diagnosis and biomedical research through at-home screening. Deep learning algorithms for portable microscopes also help to enhance diagnostic accuracy by reducing the imaging resolution gap between benchtop and portable microscopes. This review highlighted recent progress and continued efforts in a smartphone-tethered optical platform through portable, automated, and deep-learning-enabled microscopy for personalized diagnostics and remote monitoring. In detail, the optical platforms through smartphone-based microscopes and lens-free holographic microscopy are introduced, and deep learning-based portable microscopic imaging is explained to improve the image resolution and accuracy of diagnostics. The challenges and prospects of portable optical systems with microfluidic channels and a compact microscope to screen COVID-19 in the current pandemic are also discussed. It has been believed that this review offers a novel guide for rapid diagnosis, biomedical imaging, and digital healthcare with low cost and portability.

Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse

The availability of cost-effective, highly portable, and easy to use high-resolution live-cell imaging systems could present a significant technological break-through in challenging environments, such as high-level biosafety laboratories or sites where new viral outbreaks are suspected. We describe and demonstrate a cost-effective high-speed fluorescence microscope enabling the live tracking of virus particles across virological synapses that form between infected and uninfected T cells. The dynamics of HIV-1 proteins studied at the cellular level and the formation of virological synapses in living T cells reveals mechanisms by which cell-cell interactions facilitate infection between immune cells. Dual-color 3D fluorescence deconvolution microscopy of HIV-1 particles at frames rates of 100 frames per second allows us to follow the transfer of HIV-1 particles across the T cell virological synapse between living T cells. We also confirm the successful transfer of virus by imaging T cell samples fixed at specific time points during cell-cell virus transfer by super-resolution structured illumination microscopy.

A Smartphone-Based Low-Cost Inverted Laser Fluorescence Microscope for Disease Diagnosis

Fluorescence microscopy is an important tool for disease diagnosis, often requiring costly optical components, such as fluorescence filter cubes and high-power light sources. Due to its high cost, conventional fluorescence microscopy cannot be fully exploited in low-income settings. Smartphone-based fluorescence microscopy becomes an interesting low-cost alternative, but raises challenges in the optical system. We present the development of a low-cost inverted laser fluorescence microscope that uses a smartphone to visualize the fluorescence image of biological samples. Our fluorescence microscope uses a laser-based simplified optical filter system that provides analog optical filtering capabilities of a fluorescence filter cube. Firstly, we validated our inverted optical filtering by visualizing microbeads labeled with three different fluorescent compounds or fluorophores commonly used for disease diagnosis. Secondly, we validated the disease diagnosis capabilities by comparing the results of our device with those of a commercial fluorescence microscope. We successfully detected and visualized Trypanosoma cruzi parasites, responsible for the Chagas infectious disease and the presence of Antineutrophil cytoplasmic antibodies of the ANCA non-communicable autoimmune disease. The samples were labeled with the fluorescein isothiocyanate (FITC) fluorophore, one of the most commonly used fluorophores for disease diagnosis. Our device provides a 400× magnification and is at least one order of magnitude cheaper than conventional commercial fluorescence microscopes.

Construction of ratiometric fluorescence sensor and test strip with smartphone based on molecularly imprinted dual-emission quantum dots for the selective and sensitive detection of domoic acid

Domoic acid (DA), a highly neurotoxic metabolite produced by phytoplankton, contaminates seafood products and threats humankind. Herein, we have proposed a molecular imprinting fluorescence sensor with internal standard ratiometric mode for sensing of DA in seafood and seawater. In this study, the silicon-coated blue luminous carbon dots (B-CDs@SiO₂) and CdTe acted as reference probe (430 nm) and response probe (610 nm), respectively. Subsequently, the two probes were assembled and the molecularly imprinted polymer (MIP) was introduced as the recognition element to construct the core component of the sensor (B-CDs@SiO₂/CdTe MIP). When DA exists, it can be specifically adsorbed by the amino-rich imprinted sites on surface of B-CDs@SiO₂/CdTe MIP and further assembled into the hydrogen-bonds complex, which can lead to the decrease in the fluorescence signal of MIP at 610 nm owing to the electron transfer from CdTe to DA. However, the fluorescence signal of MIP at 430 nm is not affected because of the protection of silica layer. Based on this principle, the designed internal standard ratiometric fluorescence sensor reveals high sensitivity, excellent selectivity, and wide linear range of 0.03-1 μM with a detection limit of 18 nM. Further, the portable fluorescent test strip with smartphone has been designed for semi-quantitative sensing of DA, which has potential application prospects for field analysis.

Application of Fluorescence In Situ Hybridization Assisted by Fluorescence Microscope in Detection of Her2 Gene in Breast Cancer Patients

In order to study the important factors for evaluating the prognosis of breast cancer patients, a fluorescence microscopy-assisted fluorescence in situ hybridization technique was proposed. Compared with other detection techniques, fluorescence in situ hybridization (FISH) technology assisted by a fluorescence microscope has gradually gained favor in related fields due to its advantages of high detection specificity, high sensitivity, and strong experimental period. Combined with the basic overview of fluorescence microscopy and FISH technology, the advantages and application points of FISH technology assisted by fluorescence microscopy in the detection of the Her2 gene in breast cancer patients were studied and discussed. The results show that IHC can be used as the primary screening for HER2 gene status detection; IHC (2+) and IHC (3+) have false positives, which are related to chromosome 17 polysomy, so FISH should be done to confirm the diagnosis.

Multi-Mode Compact Microscopy for High-Contrast and High-Resolution Imaging

We report a multi-mode compact microscope (MCM) for high-contrast and high-resolution imaging. The MCM consists of two LED illuminations, a magnification lens, a lift stage, and a housing with image processing and LED control boards. The MCM allows multi-modal imaging, including reflection, transmission, and higher magnification modes. The dual illuminations also provide high-contrast imaging of various targets such as biological samples and microcircuits. The high dynamic range (HDR) imaging reconstruction of MCM increases the dynamic range of the acquired images by 1.36 times. The microlens array (MLA)-assisted MCM also improves image resolution through the magnified virtual image of MLA. The MLA-assisted MCM successfully provides a clear, magnified image by integrating a pinhole mask to prevent image overlap without additional alignment. The magnification of MLA-assisted MCM was increased by 3.92 times compared with that of MCM, and the higher magnification mode demonstrates the image resolution of 2.46 μm. The compact portable microscope can provide a new platform for defect inspection or disease detection on site.

Anatomics: High-throughput phenotyping of plant anatomy

Anatomics is a novel phenotyping strategy focused on high-throughput imaging and quantification of plant anatomy from field-grown plants. Here we highlight its potential applications for genetic and physiological analysis of plant anatomical phenotypes.

A Cost-Effective Nucleic Acid Detection System Using a Portable Microscopic Device

A fluorescence microscope is one of the most important tools for biomedical research and laboratory diagnosis. However, its high cost and bulky size hinder the application of laboratory microscopes in space-limited and low-resource applications. Here, in this work, we proposed a portable and cost-effective fluorescence microscope. Assembled from a set of 3D print components and a webcam, it consists of a three-degree-of-freedom sliding platform and a microscopic imaging system. The microscope is capable of bright-field and fluorescence imaging with micron-level resolution. The resolution and field of view of the microscope were evaluated. Compared with a laboratory-grade inverted fluorescence microscope, the portable microscope shows satisfactory performance, both in the bright-field and fluorescence mode. From the configurations of local resources, the microscope costs around USD 100 to assemble. To demonstrate the capability of the portable fluorescence microscope, we proposed a quantitative polymerase chain reaction experiment for meat product authenticating applications. The portable and low-cost microscope platform demonstrates the benefits in space-constrained environments and shows high potential in telemedicine, point-of-care testing, and more.

Slide Over: Advances in Slide-Free Optical Microscopy as Drivers of Diagnostic Pathology

Conventional analysis using clinical histopathology is based on bright-field microscopy of thinly sliced tissue specimens. Although bright-field microscopy is a simple and robust method of examining microscope slides, the preparation of the slides needed is a lengthy and labor-intensive process. Slide-free histopathology, however, uses direct imaging of intact, minimally processed tissue samples using advanced optical-imaging systems, bypassing the extended workflow now required for the preparation of tissue sections. This article explains the technical basis of slide-free microscopy, reviews common slide-free optical microscopy techniques, and discusses the opportunities and challenges involved in clinical implementation.

Smartphone-microfluidic fluorescence imaging system for studying islet physiology

Smartphone technology has been recently applied for biomedical image acquisition and data analysis due to its high-quality imaging capability, and flexibility to customize multi-purpose apps. In this work, we developed and characterized a smartphone-microfluidic fluorescence imaging system for studying the physiology of pancreatic islets. We further evaluated the system capability by performing real-time fluorescence imaging on mouse islets labeled with either chemical fluorescence dyes or genetically encoded fluorescent protein indicators (GEFPIs). Our results showed that the system was capable of analyzing key beta-cell insulin stimulator-release coupling factors in response to various stimuli with high-resolution dynamics. Furthermore, the integration of a microfluidics allowed high-resolution detection of insulin secretion at single islet level. When compared to conventional fluorescence microscopes and macro islet perifusion apparatus, the system has the advantages of low cost, portable, and easy to operate. With all of these features, we envision that this smartphone-microfluidic fluorescence imaging system can be applied to study islet physiology and clinical applications.

Ultracompact fluorescence smartphone attachment using built-in optics for protoporphyrin-IX quantification in skin

Smartphone-based fluorescence imaging systems have the potential to provide convenient quantitative image guidance at the point of care. However, common approaches have required the addition of complex optical attachments, which reduce translation potential. In this study, a simple clip-on attachment appropriate for fluorescence imaging of protoporphyrin-IX (PpIX) in skin was designed using the built-in light source and ultrawide camera sensor of a smartphone. Software control for image acquisition and quantitative analysis was developed using the 10-bit video capability of the phone. Optical performance was characterized using PpIX in liquid tissue phantoms and endogenously produced PpIX in mice and human skin. The proposed system achieves a very compact form factor (<30 cm3) and can be readily fabricated using widely available low-cost materials. The limit of detection of PpIX in optical phantoms was <10 nM, with good signal linearity from 10 to 1000 nM (R2 >0.99). Both murine and human skin imaging verified that in vivo PpIX fluorescence was detected within 1 hour of applying aminolevulinic acid (ALA) gel. This ultracompact handheld system for quantification of PpIX in skin is well-suited for dermatology clinical workflows. Due to its simplicity and form factor, the proposed system can be readily adapted for use with other smartphone devices and fluorescence imaging applications. Hardware design and software for the system is made freely available on GitHub (https://github.com/optmed/CompactFluorescenceCam).

Improved microscopy with ultraviolet surface excitation (MUSE) using high-index immersion illumination

Microscopy with ultraviolet surface excitation (MUSE) typically has an optical sectioning thickness significantly larger than standard physical sectioning thickness, resulting in increased background fluorescence and higher feature density compared to formalin-fixed, paraffin-embedded physical sections. We demonstrate that high-index immersion with angled illumination significantly reduces optical sectioning thickness through increased angle of refraction of excitation light at the tissue interface. We present a novel objective dipping cap and waveguide-based MUSE illuminator design with high-index immersion and quantify the improvement in optical sectioning thickness, demonstrating an e^-1 section thickness reduction to 6.67 µm in tissue. Simultaneously, the waveguide illuminator can be combined with high or low magnification objectives, and we demonstrate a 6 mm² field of view, wider than a conventional 10x pathology objective. Finally, we show that resolution and contrast can be further improved using deconvolution and focal stacking, enabling imaging that is robust to irregular surface profiles on surgical specimens.