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Cell detection and classification is a key technique for disease diagnosis, but conventional methods such as optical microscopy and flow cytometry have limitations in terms of field-of-view (FOV), throughput, cost, size, and operation complexity. Lensless holographic imaging is a promising alternative that offers large FOV, rich information content, and simple structure. However, its performance on cell detection and classification still needs to be improved. In this paper, we propose an intelligent cell detection system based on lensless holographic imaging and deep learning. Our system uses unstained cells suspended in solution as samples and employs a threshold segmentation-based auto-focusing algorithm to determine the optimal focusing distance for each imaging session. We also use a deep learning-based object detection neural network to classify different types of cells from the focused holographic images without the need for cell segmentation. We demonstrated the performance of our system using four cell detection tasks: tumor cells vs. polystyrene microspheres (77.6% accuracy), different tumor cells (80.1% accuracy), red blood cells vs. white blood cells (78.1% accuracy), white blood cell subtypes (88% accuracy), which showed that our system achieved high accuracy with label-free, portable, intelligent, and fast cell detection capabilities. It has potential applications in the miniaturized cell detection field.

White blood cell (WBC) classification is a valuable diagnostic approach for identifying diseases. However, conventional methods for WBC detection, such as flow cytometers, have limitations in terms of their high cost, large system size, and laborious staining procedures. As a result, deep learning-based label-free WBC image analysis methods are gaining popularity. Nevertheless, most existing deep learning WBC classification techniques fail to effectively utilize the subtle differences in the internal structures of WBCs observed under a microscope. To address this issue, we propose a neural network with feature fusion in this study, which enables the detection of label-free WBCs. Unlike conventional convolutional neural networks (CNNs), our approach combines low-level features extracted by shallow layers with high-level features extracted by deep layers, generating fused features for accurate bright-field WBC identification. Our method achieves an accuracy of 80.3 % on the testing set, demonstrating a potential solution for deep-learning-based biomedical diagnoses. Considering the proposed method simplifies the cell detection process and eliminates the need for complex operations like fluorescent staining, we anticipate that this automatic and label-free WBC classification network could facilitate more precise and effective analysis, and it could contribute to the future adoption of miniatured flow cytometers for point-of-care (POC) diagnostics applications.

Cell dielectric property measurement holds significant potential for application in cell detection and diagnosis due to its label-free and noninvasive nature. In this study, we developed a biosensor designed to measure the permittivity of liquid samples, particularly cell suspensions at the nanoliter scale, utilizing microwave and millimeter wave coplanar waveguides in conjunction with a microchannel. This biosensor facilitates the measurement of scattering parameters within a frequency domain ranging from 1 GHz to 110 GHz. The obtained scattering parameters are then converted into dielectric constants using specific algorithms. A cell capture structure within the microchannel ensures that cell suspensions remain stable within the measurement zone. The feasibility of this biosensor was confirmed by comparison with a commercial Keysight probe. We measured the dielectric constants of three different cell suspensions (HepG2, A549, MCF-7) using our biosensor. We also counted the number of cells captured in multiple measurements for each cell type and compared the corresponding changes in permittivity. The results indicated that the real part of the permittivity of HepG2 cells is 0.2–0.8 lower than that of the other two cell types. The difference between A549 and MCF-7 was relatively minor, only 0.2–0.4. The fluctuations in the dielectric spectrum caused by changes in cell numbers during measurements were smaller than the differences observed between different cell types. Thus, the sensor is suitable for measuring cell suspensions and can be utilized for label-free, noninvasive studies in identifying biological cell suspensions.

Limited research has focused on nanoparticle (NP) applications’ impact on edible wheat parts in a field environment. Here, we studied the nutritional quality of edible parts of wheat (Triticum aestivum L.) with a field experiment by spraying MnFe2O4 nanoparticles. Wheat was foliar sprayed with 0, 25, 50, and 100 mg/L composite manganese ferrite (MnFe2O4) NPs during 220 d of a growth period. Ionic controls were prepared using the conventional counterparts (MnSO4·H2O and FeSO4·7H2O) to compare with the 100 mg/L MnFe2O4 NPs. After three consecutive foliar applications, nanoparticles demonstrated a substantial elevation in grain yield and harvest index, exhibiting a noteworthy increase to 5.0 ± 0.12 t/ha and 0.46 ± 0.001 in the 100 mg/L NP dose, respectively, concomitant with a 14% enhancement in the grain number per spike. Fe, Mn, and Ca content in grain increased to 77 ± 2.7 mg/kg, 119 ± 2.8 mg/kg, and 0.32 ± 7.9 g/kg in the 100 mg/L NPs, respectively. Compared to the ion treatment, the 100 mg/L NP treatments notably boosts wheat grain crude protein content (from 13 ± 0.79% to 15 ± 0.58%) and effectively lowers PA/Fe levels (from 11 ± 0.7 to 9.3 ± 0.5), thereby improving Fe bioavailability. The VSM results exhibited a slight superparamagnetic behavior, whereas the grains and stems exhibited diamagnetic behavior. The results indicate that the nanomaterial did not accumulate in the grains, suggesting its suitability as an Fe and Mn-rich fertilizer in agriculture. Above all, the foliar application of nanocomposites increased the concentrations of Fe, Mn, and Ca in wheat grains, accompanied by a significant enhancement in grain yield. Therefore, the research results indicate that the foliar application of MnFe2O4 NPs can positively regulate wheat grains’ nutritional quality and yield.

Manganese dioxide (MnO2) is a widely used and well-studied 3-dimensional (3D) transition metal oxide, which has advantages in ultrafast optics due to large specific surface area, narrow bandgap, multiple pores, superior electron transfer capability, and a wide range of light absorption. However, few studies have considered its excellent performance in ultrafast photonics. γ-MnO2 photonics devices were fabricated based on a special dual-core, pair-hole fiber (DCPHF) carrier and applied in ultrafast optics fields for the first time. The results show that the soliton molecule with tunable temporal separation (1.84 to 2.7 ps) and 600-MHz harmonic solitons are achieved in the experiment. The result proves that this kind of photonics device has good applications in ultrafast lasers, high-performance sensors, fiber optical communications, etc., which can help expand the prospect of combining 3D materials with novel fiber for ultrafast optics device technology.

The differential count of white blood cells (WBCs) can effectively provide disease information for patients. Existing stained microscopic WBC classification usually requires complex sample-preparation steps, and is easily affected by external conditions such as illumination. In contrast, the inconspicuous nuclei of stain-free WBCs also bring great challenges to WBC classification. As such, image enhancement, as one of the preprocessing methods of image classification, is essential in improving the image qualities of stain-free WBCs. However, traditional or existing convolutional neural network (CNN)-based image enhancement techniques are typically designed as standalone modules aimed at improving the perceptual quality of humans, without considering their impact on advanced computer vision tasks of classification. Therefore, this work proposes a novel model, UR-Net, which consists of an image enhancement network framed by ResUNet with an attention mechanism and a ResNet classification network. The enhancement model is integrated into the classification model for joint training to improve the classification performance for stain-free WBCs. The experimental results demonstrate that compared to the models without image enhancement and previous enhancement and classification models, our proposed model achieved a best classification performance of 83.34% on our stain-free WBC dataset.

The manipulation of biomedical particles, such as separating circulating tumor cells from blood, based on standing surface acoustic wave (SSAW) has been widely used due to its advantages of label-free approaches and good biocompatibility. However, most of the existing SSAW-based separation technologies are dedicated to isolate bioparticles in only two different sizes. It is still challenging to fractionate various particles in more than two different sizes with high efficiency and accuracy. In this work, to tackle the problems of low efficiency for multiple cell particle separation, integrated multi-stage SSAW devices with different wavelengths driven by modulated signals were designed and studied. A three-dimensional microfluidic device model was proposed and analyzed using the finite element method (FEM). In addition, the effect of the slanted angle, acoustic pressure, and the resonant frequency of the SAW device on the particle separation were systemically studied. From the theoretical results, the separation efficiency of three different size particles based on the multi-stage SSAW devices reached 99%, which was significantly improved compared with conventional single-stage SSAW devices.

The conventional high-resolution optical microscopy for biomedical imaging has high cost and low portability due to bulky optical lenses system.To realize miniaturized imaging,the recent advance of lensless microfluidic on-chip imaging technique based on the integration of CMOS image sensor and microfluidics has provided one promising solution.Therefore,the architecture,principle,problems and improvement solutions for different miniaturized lensless microfluidic on-chip bio-imaging,including shadow imaging,holographic imaging,fluorescent imaging and color imaging were reviewed.And the deficiencies and possible development directions were discussed.

With the ubiquitous deployment of smartphones worldwide recently, biomedical diagnostic devices based on smartphones are potential to be widely used for smart health management on the Internet of Medical Things (IoMT) applications under point-of-care (PoC) settings. Therefore, this paper introduces one smartphone-based blood lipid data acquisition dongle to monitor blood lipid level, including total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C), using only a fingertip blood drop for cardiovascular disease management. During detection, a thin photochemical test strip that consists of LEDs and detectors is plugged into the compact dongle, where changed color intensities will indicate the lipid determinant concentration from the measured reflection coefficient of the chromogenic product. Such smartphone-based photochemical blood lipid data acquisition dongle was validated by comparing with a clinical analyzer, and reached a correlation coefficient over 0.903 and averaged coefficient of variation (CV)% of 4.575% for blood lipid measurement of 115 patients. The photochemical dongle is, therefore, promising for the future chronic disease management in the IoMT.

Abstract The lattice stability and phonon response of Ti3C2T x MXene at high pressure are important for understanding its mechanical and thermal properties fully. Here, we use in situ high hydrostatic pressure X-ray diffraction (XRD) and Raman spectroscopy to study the lattice deformation and phonon behavior of Ti3C2T x MXene. XRD spectra indicate that no phase transformation occurs up to the pressure of 26.7 GPa. The elastic constant along a lattice parameter was calculated to be 378 GPa. In the Raman spectra obtained at high-pressure, the out-of-plane phonon modes (A 1g at ~ 210, ~ 504, and ~ 711 cm−1) exhibit monotonic blueshifts with increasing pressure. The Grüneisen parameters of these three modes were calculated to be 1.08, 1.16, and 0.29, respectively. These results enrich the basic property data of Ti3C2T x MXene and would benefit the further understanding of this novel material.

Traditional Chinese medicine (TCM) is based on a unique disease diagnosis and treatment system that has been developed over the last 2,300 years. In the TCM, “syndrome differentiation and treatment”(SDAT) is a core method for doctors to deal with diseases. This diagnostic and therapeutic technique that infer the occurrence and the development of diseases by observing symptoms as a whole, not only has its own uniqueness but also has been recognized by the public in oriented medical fields for its clinical efficacy. With recent developments in computer science, the Internet, big data, and artificial intelligence, a study based on the SDAT algorithm has aroused much attention. This paper encompasses three stages spanning 30 years to accomplish the following: 1) the TCM data and the modern SDAT system were collated and summarized based on 35,706 reference data on the TCM, starting from the syndrome differentiation of four aspects, such as the cause, location, characteristics, and conditions of the disease (CLCC), we constructed a quantitative model of the TCM SDAT regarding the CLCC of the disease, collected the symptom information on the diagnosed subject, and transferred them to the SDAT assistant algorithm for calculation and analysis, to determine the CLCC, Based on the therapy recommended by the differentiation results in the knowledge base and the prescription and traditional Chinese medicines recommended by the therapy, any stage of all diseases could determine a syndrome type by differentiating the CLCC, we constructed the basic SDAT algorithm integrating theory, method, prescription, and medicine and realized the calculability in the TCM diagnosis and treatment process; 2) based on the SDAT algorithm, we developed the TCM doctor's workstation software and introduced it to more than 80 TCM institutions in Sichuan province, China, we collated a large-scale trove of samples of the TCM data platform that was established with more than 2.9 million TCM electronic medical records (EMRs) and reference data, and had the compliance tested and algorithm verified on the 9,300 EMRs of the common diseases in the TCM; and 3) based on the dimension reduction and degree elevation optimization of the technology with a directed graph to the basic algorithm, the algorithm complexity was reduced and the accuracy of the algorithm was improved. It was demonstrated that the coincidence rate of the basic model was 80.47% and the basic coincidence rate was 96.19%. After optimizing the basic algorithm (for example, for gastric abscess), the coincidence rate increased by 7.04%. The test results demonstrated the efficacy of the model study. This model realized a computable SDAT to specify and assist in the differentiation diagnosis and in the treatment processes of the TCM and improve the service quality of the TCM diagnosis and treatment.

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The lensless on-chip microscope is an emerging technology in the recent decade that can realize the imaging and analysis of biological samples with a wide field-of-view without huge optical devices and any lenses. Because of its small size, low cost, and being easy to hold and operate, it can be used as an alternative tool for large microscopes in resource-poor or remote areas, which is of great significance for the diagnosis, treatment, and prevention of diseases. To improve the low-resolution characteristics of the existing lensless shadow imaging systems and to meet the high-resolution needs of point-of-care testing, here, we propose a high-precision on-chip microscope based on in-line holographic technology. We demonstrated the ability of the iterative phase recovery algorithm to recover sample information and evaluated it with image quality evaluation algorithms with or without reference. The results showed that the resolution of the holographic image after iterative phase recovery is 1.41 times that of traditional shadow imaging. Moreover, we used machine learning tools to identify and count the mixed samples of mouse ascites tumor cells and micro-particles that were iterative phase recovered. The results showed that the on-chip cell counter had high-precision counting characteristics as compared with manual counting of the microscope reference image. Therefore, the proposed high-precision lensless microscope on a chip based on in-line holographic imaging provides one promising solution for future point-of-care testing (POCT).

The differential count of white blood cells (WBCs) is one widely used approach to assess the status of a patient’s immune system. Currently, the main methods of differential WBC counting are manual counting and automatic instrument analysis with labeling preprocessing. But these two methods are complicated to operate and may interfere with the physiological states of cells. Therefore, we propose a deep learning-based method to perform label-free classification of three types of WBCs based on their morphologies to judge the activated or inactivated neutrophils. Over 90% accuracy was finally achieved by a pre-trained fine-tuning Resnet-50 network. This deep learning-based method for label-free WBC classification can tackle the problem of complex instrumental operation and interference of fluorescent labeling to the physiological states of the cells, which is promising for future point-of-care applications.

A scientific review is a type of article that summarizes the current state of a specific field, which is crucial for promoting the advancement of our science community. Authors need to read hundreds of research articles to prepare the data and insights for a comprehensive review, which is time-consuming and labor-intensive. In this work, we present an algorithm that can automatically extract keywords from the meta-information of each article and generate the basic data for review articles. Two different fields—communication engineering, and lab on a chip technology—were analyzed as examples. We first built an article library by downloading all the articles from the target journal using a python-based crawler. Second, the rapid automatic keyword extraction algorithm was implemented on the title and abstract of each article. Finally, we classified all extracted keywords into class by calculating the Levenshtein distance between each of them. The results demonstrated its capability of not only finding out how communication engineering and lab on a chip were evolved in the past decades but also summarizing the analytical outcomes after data mining of the extracted keywords. Our algorithm is more than a useful tool for researchers during the preparation of a review article, it can also be applied to quantitatively analyze the past, present and help authors predict the future trend of a specific research field.

An ultra-low power CMOS image sensor with on-chip energy harvesting and power management capability is introduced in this paper. The photodiode pixel array can not only capture images but also harvest solar energy. As such, the CMOS image sensor chip is able to switch between imaging and harvesting modes towards self-power operation. Moreover, an on-chip maximum power point tracking (MPPT)-based power management system (PMS) is designed for the dual-mode image sensor to further improve the energy efficiency. A new isolated P-well energy harvesting and imaging (EHI) pixel with very high fill factor is introduced. Several ultra-low power design techniques such as reset and select boosting techniques have been utilized to maintain a wide pixel dynamic range. The chip was designed and fabricated in a 1.8 V, 1P6M 0.18 µm CMOS process. Total power consumption of the imager is 6.53 µW for a 96 × 96 pixel array with 1 V supply and 5 fps frame rate. Up to 30 μW of power could be generated by the new EHI pixels. The PMS is capable of providing 3× the power required during imaging mode with 50% efficiency allowing energy autonomous operation with a 72.5% duty cycle.

Leakage in heat exchanger tubes can result in unreliable products and dangerous situations, which could cause great economic losses. Along with fast development of modern acoustic detection technology, using acoustic signals to detect leakage in heat exchange tube has been gradually accepted and considered with great potential by both industrial and research societies. In order to further advance the development of acoustic signal detection technology and investigate better methods for leakage detection in heat exchange tube, in this paper, rstly, we conduct a short overview of the theory of acoustic signal detection on heat exchanger tube, which had already been continuously developed for a few decades by researchers worldwide. Thereafter, we further expound the advantages and limitations of acoustic signal detection technology on heat exchanger tube in four aspects: 1) principles of acoustic signal detection, 2) characteristics of sound wave propagation in heat exchanger tube, 3) methods of leakage detection, and 4) leakage localization in heat exchanger tube.

Lensless microfluidic imaging with super-resolution processing has become a promising solution to miniaturize the conventional flow cytometer for point-of-care applications. The previous multi-frame super-resolution processing system can improve resolution but has limited cell flow rate and hence low throughput when capturing multiple subpixel-shifted cell images. This paper introduces a single-frame super-resolution processing with on-line machine-learning for contact images of cells. A corresponding contact-imaging based microfluidic cytometer prototype is demonstrated for cell recognition and counting. Compared with commercial flow cytometer, less than 8% error is observed for absolute number of microbeads; and 0.10 coefficient of variation is observed for cell-ratio of mixed RBC and HepG2 cells in solution.

A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications.