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Abstract Optical microscopy has long been the standard method for diagnosis in cytopathology. Whole slide scanners can image and digitize large sample areas automatically, but are slow, expensive and therefore not widely available. Clinical diagnosis of cytology specimens is especially challenging since these samples are both spread over large areas and thick, which requires 3D capture. Here, we introduce a new parallelized microscope for scanning thick specimens across extremely wide fields-of-view (54 × 72 mm2) at 1.2 and 0.6 μm resolutions, accompanied by machine learning software to rapidly assess these 16 gigapixel scans. This Multi-Camera Array Scanner (MCAS) comprises 48 micro-cameras closely arranged to simultaneously image different areas. By capturing 624 megapixels per snapshot, the MCAS is significantly faster than most conventional whole-slide scanners. We used this system to digitize entire cytology samples (scanning three entire slides in 3D in just several minutes) and demonstrate two machine learning techniques to assist pathologists: first, an adenocarcinoma detection model in lung specimens (0.73 recall); second, a slide-level classification model of lung smears (0.969 AUC).

Abstract We present a large-scale computational 3D topographic microscope that enables 6-gigapixel profilometric 3D imaging at micron-scale resolution across >110 cm2 areas over multi-millimeter axial ranges. Our computational microscope, termed STARCAM (Scanning Topographic All-in-focus Reconstruction with a Computational Array Microscope), features a parallelized, 54-camera architecture with 3-axis translation to capture, for each sample of interest, a multi-dimensional, 2.1-terabyte (TB) dataset, consisting of a total of 224,640 9.4-megapixel images. We developed a self-supervised neural network-based algorithm for 3D reconstruction and stitching that jointly estimates an all-in-focus photometric composite and 3D height map across the entire field of view, using multi-view stereo information and image sharpness as a focal metric. The memory-efficient, compressed differentiable representation offered by the neural network effectively enables joint participation of the entire multi-TB dataset during the reconstruction process. Validation experiments on gauge blocks demonstrate a profilometric precision and accuracy of 10 µm or better. To demonstrate the broad utility of our new computational microscope, we applied STARCAM to a variety of decimeter-scale objects, with applications ranging from cultural heritage to industrial inspection.

Abstract In this report, we present the development of a copper nanofiber network-based microheater, designed for applications in electron microscopes, gas sensing, and cell culture platforms. The seed layer, essential for electroless deposition, was fabricated through the electrospinning of a palladium-contained polyvinylpyrrolidone solution followed by a heat treatment. This process minimized the contact resistance between nanofibers. We successfully fabricated a microheater with evenly distributed temperature by controlling the electrospinning time, heat treatment conditions, and electroless deposition time. We assessed the electrical and thermal characteristics of the microheater by examining the nanofiber density, sheet resistance, and transmittance. The microheater’s performance was evaluated by applying current, and we verified its capacity to heat up to a maximum of 350 °C. We further observed the microheater’s temperature distribution at varying current levels through an infrared camera. The entire manufacturing procedure takes place under normal pressure, eliminating the need for masking or etching processes. This renders the method easily adaptable to the mass production of microdevices. The method is expected to be applicable to various materials and sizes and is cost-effective compared to commercially produced microheaters developed through microelectromechanical system processes, which demand complex facilities and high cost.

Abstract Surface wetting, the phenomenon where a liquid spreads or adheres to a solid surface, plays a crucial role in both natural and technological fields. This study focuses on elucidating the relationship between surface properties and wetting behavior, emphasizing the significance of hierarchical structures. A 3D hierarchical structure is created by controlling shape and size through electroplating and chemical reactions, adjusted by current intensity, ammonium persulfate, and ammonium hydroxide concentrations. This modification is achieved by modifying the surface's chemical properties. This control directly impacted the surface wetting properties, providing a means to regulate wetting behavior by altering surface structure. Through control of surface chemistry, a superhydrophilic surface is able to successfully create with a contact angle of 0° and a superhydrophobic surface with a contact angle of 171.3.

Abstract Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining protocols face several challenges, such as a need for extensive, manual processing of samples, substantial time delays, altered tissue homeostasis, limited choice of contrast agents, 2D imaging instead of 3D tomography and many more. Label-free optical technologies, on the other hand, do not rely on exogenous and artificial markers, by exploiting intrinsic optical contrast mechanisms, where the specificity is typically less obvious to the human observer. Over the past few years, digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. In this review article, we provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.

Abstract Background Neonatal jaundice is common, and despite the considerable medical costs associated with it, there are still few studies on the maternal factors associated with it. Identification of maternal factors associated with neonatal jaundice is very important in terms of prevention, screening and management of neonatal jaundice. The current study aimed to identify maternal disease factors associated with neonatal jaundice. Methods We compared the maternal disease diagnostic codes during pregnancy (study A) and 1 year before conception (study B) in mothers whose insurance claims data included newborns treated for neonatal jaundice before birth registration via the National Health Insurance Service–National Sample Cohort (control group). To decrease the effect of confounding variables, the neonatal jaundice and control groups were matched at a ratio of 1:10 via propensity score matching using covariates including age and income. Results The matched samples for studies A and B included 4,026 and 3,278 (jaundice group: 366 and 298) delivery cases, respectively. In both studies, the jaundice group had a higher proportion of patients who underwent cesarean section than the control group. In study A, other diseases of the digestive system had the highest odds ratio (OR) (K92; adjusted OR: 14.12, 95% confidence interval [CI]: 2.70–82.26). Meanwhile, gastritis and duodenitis had the lowest OR (K29; adjusted OR: 0.39, 95% CI: 0.22–0.69). In study B, salpingitis and oophoritis had the highest OR (N70; adjusted OR: 3.33, 95% CI: 1.59–6.94). Heartburn had the lowest OR (R12; adjusted OR: 0.29, 95% CI:0.12–0.71). Conclusions This study identified maternal disease factors correlated with neonatal jaundice during pregnancy and 1 year before conception. Maternal risk factors for neonatal jaundice included syphilis and leiomyoma during pregnancy, and salpingo-oophoritis before pregnancy. The protective factors included infection, inflammatory diseases, and dyspepsia.

Superhydrophobic surfaces, i.e., surfaces with a water contact angle (WCA) ≥ 150°, have gained much attention as they are multifunctional surfaces with features such as self-cleaning, which can be useful in various applications such as those requiring waterproof and/or protective films. In this study, we prepared a solution from recycled polyethylene terephthalate (PET) and fabricated a superhydrophobic surface using electrospinning and electrospraying processes. We observed that the fabricated geometry varies depending on the solution conditions, and based on this, we fabricated a hierarchical structure. From the results, the optimized structure exhibited a very high WCA (>156.6°). Additionally, our investigation into the self-cleaning functionality and solar panel efficiency of the fabricated surface revealed promising prospects for the production of superhydrophobic surfaces utilizing recycled PET, with potential applications as protective films for solar panels. Consequently, this research contributes significantly to the advancement of environmentally friendly processes and the progress of recycling technology.

Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.

The dynamics of living organisms are organized across many spatial scales. However, current cost-effective imaging systems can measure only a subset of these scales at once. We have created a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics. By collecting data from up to 96 cameras, we computationally generate gigapixel-scale images and movies with a field of view over hundreds of square centimeters at an optical resolution of 18 µm. This allows us to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales, including larval zebrafish, fruit flies, nematodes, carpenter ants, and slime mold. Further, the MCAM architecture allows stereoscopic tracking of the z-position of organisms using the overlapping field of view from adjacent cameras. Overall, by removing the bottlenecks imposed by single-camera image acquisition systems, the MCAM provides a powerful platform for investigating detailed biological features and behavioral processes of small model organisms across a wide range of spatial scales.

A wide variety of diseases are commonly diagnosed via the visual examination of cell morphology within a peripheral blood smear. For certain diseases, such as COVID-19, morphological impact across the multitude of blood cell types is still poorly understood. In this paper, we present a multiple instance learning-based approach to aggregate high-resolution morphological information across many blood cells and cell types to automatically diagnose disease at a per-patient level. We integrated image and diagnostic information from across 236 patients to demonstrate not only that there is a significant link between blood and a patient's COVID-19 infection status, but also that novel machine learning approaches offer a powerful and scalable means to analyze peripheral blood smears. Our results both backup and enhance hematological findings relating blood cell morphology to COVID-19, and offer a high diagnostic efficacy; with a 79% accuracy and a ROC-AUC of 0.90.

Fast noninvasive probing of spatially varying decorrelating events, such as cerebral blood flow beneath the human skull, is an essential task in various scientific and clinical settings. One of the primary optical techniques used is diffuse correlation spectroscopy (DCS), whose classical implementation uses a single or few single-photon detectors, resulting in poor spatial localization accuracy and relatively low temporal resolution. Here, we propose a technique termed ClassifyingRapid decorrelationEvents viaParallelized single photon dEtection (CREPE), a new form of DCS that can probe and classify different decorrelating movements hidden underneath turbid volume with high sensitivity using parallelized speckle detection from a 32 × 32 pixel SPAD array. We evaluate our setup by classifying different spatiotemporal-decorrelating patterns hidden beneath a 5 mm tissue-like phantom made with rapidly decorrelating dynamic scattering media. Twelve multi-mode fibers are used to collect scattered light from different positions on the surface of the tissue phantom. To validate our setup, we generate perturbed decorrelation patterns by both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as well as a vessel phantom containing flowing fluid. Along with a deep contrastive learning algorithm that outperforms classic unsupervised learning methods, we demonstrate our approach can accurately detect and classify different transient decorrelation events (happening in 0.1–0.4 s) underneath turbid scattering media, without any data labeling. This has the potential to be applied to non-invasively monitor deep tissue motion patterns, for example identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates within a compact and static detection probe.

Electric fields are widely used for controlling liquids in various research fields. To control a liquid, an alternating current (AC) electric field can offer unique advantages over a direct current (DC) electric field, such as fast and programmable flows and reduced side effects, namely the generation of gas bubbles. Here, we demonstrate one-directional flow along carbon nanotube nanowires under an AC electric field, with no additional equipment or frequency matching. This phenomenon has the following characteristics: First, the flow rates of the transported liquid were changed by altering the frequency showing Gaussian behaviour. Second, a particular frequency generated maximum liquid flow. Third, flow rates with an AC electric field (approximately nanolitre per minute) were much faster than those of a DC electric field (approximately picolitre per minute). Fourth, the flow rates could be controlled by changing the applied voltage, frequency, ion concentration of the solution and offset voltage. Our finding of microfluidic control using an AC electric field could provide a new method for controlling liquids in various research fields.

Oblique submicron-scale structures are used in various aspects of research, such as the directional characteristics of dry adhesives and wettability. Although deposition, etching, and lithography techniques are applied to fabricate oblique submicron-scale structures, these approaches have the problem of the controllability or throughput of the structures. Here, we propose a simple X-ray-lithography method, which can control the oblique angle of submicron-scale structures with areas on the centimeter scale. An X-ray mask was fabricated by gold film deposition on slanted structures. Using this mask, oblique ZEP520A photoresist structures with slopes of 20° and 10° and widths of 510 nm and 345 nm were fabricated by oblique X-ray exposure, and the possibility of polydimethylsiloxane (PDMS) molding was also confirmed. In addition, through double exposure with submicron- and micron-scale X-ray masks, dotted-line patterns were produced as an example of multiscale patterning.

Brain‒machine interface (BMI) is a promising technology that looks set to contribute to the development of artificial limbs and new input devices by integrating various recent technological advances, including neural electrodes, wireless communication, signal analysis, and robot control. Neural electrodes are a key technological component of BMI, as they can record the rapid and numerous signals emitted by neurons. To receive stable, consistent, and accurate signals, electrodes are designed in accordance with various templates using diverse materials. With the development of microelectromechanical systems (MEMS) technology, electrodes have become more integrated, and their performance has gradually evolved through surface modification and advances in biotechnology. In this paper, we review the development of the extracellular/intracellular type of in vitro microelectrode array (MEA) to investigate neural interface technology and the penetrating/surface (non-penetrating) type of in vivo electrodes. We briefly examine the history and study the recently developed shapes and various uses of the electrode. Also, electrode materials and surface modification techniques are reviewed to measure high-quality neural signals that can be used in BMI.

In this paper, we developed a method of fabricating polymer microstructures at various angles on a single substrate via synchrotron X-ray lithography coupled with simple dimensional transformations. Earlier efforts to create various three-dimensional (3D) features on flat substrates focused on the exposure technology, material properties, and light sources. A few research groups have sought to create microstructures on curved substrates. We created tilted microstructures of various angles by simply deforming the substrate from 3D to two-dimensional (2D). The microstructural inclination angles changed depending on the angles of the support at particular positions. We used convex, concave, and S-shaped supports to fabricate microstructures with high aspect ratios (1:11) and high inclination angles (to 79°). The method is simple and can be extended to various 3D microstructural applications; for example, the fabrication of microarrays for optical components, and tilted micro/nanochannels for biological applications.