Recent advances in deformation-assisted microfluidic cell sorting technologies.

Cell sorting is an essential prerequisite for cell research and has great value in life science and clinical studies. Among the many microfluidic cell sorting technologies, label-free methods based on the size of different cell types have been widely studied. However, the heterogeneity in size for cells of the same type and the inevitable size overlap between different types of cells would result in performance degradation in size-based sorting. To tackle such challenges, deformation-assisted technologies are receiving more attention recently. Cell deformability is an inherent biophysical marker of cells that reflects the changes in their internal structures and physiological states. It provides additional dimensional information for cell sorting besides size. Therefore, in this review, we summarize the recent advances in deformation-assisted microfluidic cell sorting technologies. According to how the deformability is characterized and the form in which the force acts, the technologies can be divided into two categories: (1) the indirect category including transit-time-based and image-based methods, and (2) the direct category including microstructure-based and hydrodynamics-based methods. Finally, the separation performance and the application scenarios of each method, the existing challenges and future outlook are discussed. Deformation-assisted microfluidic cell sorting technologies are expected to realize greater potential in the label-free analysis of cells.

Droplet microarray platforms for high-throughput drug screening.

High-throughput screening platforms are fundamental for the rapid and efficient processing of large amounts of experimental data. Parallelization and miniaturization of experiments are important for improving their cost-effectiveness. The development of miniaturized high-throughput screening platforms is essential in the fields of biotechnology, medicine, and pharmacology. Currently, most laboratories use 96- or 384-well microtiter plates for screening; however, they have disadvantages, such as high reagent and cell consumption, low throughput, and inability to avoid cross-contamination, which need to be further optimized. Droplet microarrays, as novel screening platforms, can effectively avoid these shortcomings. Here, the preparation method of the droplet microarray, method of adding compounds in parallel, and means to read the results are briefly described. Next, the latest research on droplet microarray platforms in biomedicine is presented, including their application in high-throughput culture, cell screening, high-throughput nucleic acid screening, drug development, and individualized medicine. Finally, the challenges and future trends in droplet microarray technology are summarized.

Numerical Study of Particle Separation through Integrated Multi-Stage Surface Acoustic Waves and Modulated Driving Signals.

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.

UR-Net: An Integrated ResUNet and Attention Based Image Enhancement and Classification Network for Stain-Free White Blood Cells.

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.

Rapid and Label-Free Classification of Blood Leukocytes for Immune State Monitoring.

A fully automated and label-free sample-to-answer white blood cell (WBC) cytometry platform for rapid immune state monitoring is demonstrated. The platform integrates (1) a WBC separation process using the multidimensional double spiral (MDDS) device and (2) an imaging process where images of the separated WBCs are captured and analyzed. Using the deep-learning-based image processing technique, we analyzed the captured bright-field images to classify the WBCs into their subtypes. Furthermore, in addition to cell classification, we can detect activation-induced morphological changes in WBCs for functional immune assessment, which could allow the early detection of various diseases. The integrated platform operates in a rapid (<30 min), fully automated, and label-free manner. The platform could provide a promising solution to future point-of-care WBC diagnostics applications.

Recent progress of inertial microfluidic-based cell separation.

Cell separation has consistently been a pivotal technology of sample preparation in biomedical research. Compared with conventional bulky cell separation technologies applied in the clinic, cell separation based on microfluidics can accurately manipulate the displacement of liquid or cells at the microscale, which has great potential in point-of-care testing (POCT) applications due to small device size, low cost, low sample consumption, and high operating accuracy. Among various microfluidic cell separation technologies, inertial microfluidics has attracted great attention due to its simple structure and high throughput. In recent years, many researchers have explored the principles and applications of inertial microfluidics and developed different channel structures, including straight channels, curved channels, and multistage channels. However, the recently developed multistage channels have not been discussed and classified in detail compared with more widely discussed straight and curved channels. Therefore, in this review, a comprehensive and detailed review of recent progress in the multistage channel is presented. According to the channel structure, the inertial microfluidic separation technology is divided into (i) straight channel, (ii) curved channel, (iii) composite channel, and (iv) integrated device. The structural development of straight and curved channels is discussed in detail. And based on straight and curved channels, the multistage cell separation structures are reviewed, with a special focus on a variety of latest structures and related innovations of composite and integrated channels. Finally, the future prospects for the existing challenges in the development of inertial microfluidic cell separation technology are presented.

Correction: Huang et al. Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring. Sensors 2021, 21, 512.

The authors wish to make the following correction to their paper [...].

Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.

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.

High-Precision Lensless Microscope on a Chip Based on In-Line Holographic Imaging.

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).

Identification of Different Bile Species and Fermentation Times of Bile Arisaema Based on an Intelligent Electronic Nose and Least Squares Support Vector Machine.

Fermentation is one of the most traditionally utilized methods to process the raw materials of traditional Chinese medicine (TCM). Bile Arisaema (BA) is produced by the fermentation of the roots of Arisaema heterophyllum with bile. Fermentation time and bile species are the key factors in producing BA. The study was aimed to develop a new and rapid method for the identification of different fermentation times and bile species of BA. The polysaccharide content (PC), protease activity (PA), and amylase activity (AC) of BA were determined. The changes of PC, PA, and AC were significant indicators for the evaluation of different fermentation times. On the basis of the odor data of BA obtained by electronic nose technology (E-nose), the principal component analysis (PCA) was used to identify bile species. The results were further verified by the least squares support vector machine (LS-SVM). The trained LS-SVM was also used to predict the PC, PA, and AC of the samples to identify fermentation time. The present study indicated that E-nose combined with LS-SVM could effectively predict the PC, PA, and AC of the samples and identify the bile species and fermentation time of BA, and it was proved to be a useful strategy for quality control of fermented products of TCMs.

Gold surface plasmon crystal structure based-on polystyrene template for biosensor application.

In this communication, we assembled ordered polystyrene (PS) microsphere array as a template with the drop-coating method, and the oxygen plasma was used to etch the template to adjust the spacing between the PS microspheres. Nano-triangular gold array and silver nano-pyramid array were obtained by ion beam sputtering to deposit precious metal gold and silver. We observed the surface morphology of Au and Au/Ag composite films by scanning electron microscope and characterized the films by X-ray diffraction and ultraviolet/visible light spectrophotometer. The results show that the etching time of oxygen plasma has an obvious effect in adjusting the spacing between PSs and has a significant effect on the morphology of Au structure.

Smartphone-based analytical biosensors.

The traditional analytical biosensor instruments are relatively bulky, expensive, and not easy to handle, thus their applications are largely limited in resource-limited settings. The recent development of microfluidic lab-on-a-chip (LOC) technology has provided a possible solution to miniaturize the conventional biosensing system, yet other accessory devices to detect, readout, analyze, transfer, and display results are still required. With the rapid development, mass production, and pervasive distribution of smartphones in recent years, they have provided people with portable, cost-effective, and easy-to-operate platforms to build analytical biosensors for point-of-care (POC) applications and mobile health. Based on the common analytical methods, this paper reviews the recent development of four types of smartphone based analytical biosensory systems at the POC, i.e., smartphone-based microscopic imaging, colorimetric, electrochemical, and electrochemiluminescence biosensor. The different bio-sensing strategies and analytical performance together with future perspectives are discussed.

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting.

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.

On-Demand Lensless Single Cell Imaging Activated by Differential Resistive Pulse Sensing.

We present an on-demand single cell imaging technique activated by differential resistive pulse sensing in a portable system integrating a microfluidic differential coulter counter and a lensless complementary metal-oxide-semiconductor (CMOS) imaging sensor. Dual parametric single cell analysis and on-demand single cell imaging have been demonstrated by microbeads of different sizes and a cell mixture including red blood cells (RBCs) and tumor cell line HepG2 cells. The on-demand imaging capability could avoid generating useless images without cells and enable selective imaging of single cells within a specific size range.

Dual characterization of biological cells by optofluidic microscope and resistive pulse sensor.

Label-free detection technique has emerged as a powerful platform for biomedical applications since it can avoid laborious multi-step sample preparation. In this paper, we demonstrate a dual analysis of biological cells using a single microfluidic system combining optofluidic microscopy and resistive pulse sensing. Both red blood cells (RBCs) and circulating tumor cells (CTCs) have been used to validate the concept of dual analysis and also to test the performance of the microfluidic device. The cell characterization by resistive pulse sensing is in good agreement with the analysis by optofluidic microscopy, further verified by the commercial Beckman-Coulter® FC500 flow cytometry. The present system has attractive merits such as simple fabrication, easy integration, high portability, and low cost. This study has great potentials for the development of innovative on-chip flow cytometry with concurrent imaging sensing and resistive sensing.

An ultra-low power CMOS image sensor with on-chip energy harvesting and power management capability.

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.

A significant presence in atherosclerotic cardiovascular disease: Remnant cholesterol: A review.

The current first-line treatment for atherosclerotic cardiovascular disease (ASCVD) involves the reduction of a patient's low-density lipoprotein cholesterol (LDL-C) levels through the use of lipid-lowering drugs. However, even when other risk factors such as hypertension and diabetes are effectively managed, there remains a residual cardiovascular risk in these patients despite achieving target LDL-C levels with statins and new lipid-lowering medications. This risk was previously believed to be associated with lipid components other than LDL, such as triglycerides. However, recent studies have unveiled the crucial role of remnant cholesterol (RC) in atherosclerosis, not just triglycerides. The metabolized product of triglyceride-rich lipoproteins is referred to as triglyceride-rich remnant lipoprotein particles, and its cholesterol component is known as RC. Numerous pieces of evidence from epidemiological investigations and genetic studies demonstrate that RC plays a significant role in predicting the incidence of ASCVD. As a novel marker for atherosclerosis prediction, when LDL-C is appropriately controlled, RC should be prioritized for attention and intervention among individuals at high risk of ASCVD. Therefore, reducing RC levels through the use of various lipid-lowering drugs may yield long-term benefits. Nevertheless, routine testing of RC in clinical practice remains controversial, necessitating further research on the treatment of elevated RC levels to evaluate the advantages of reducing RC in patients at high risk of ASCVD.

Nanotechnology in Agriculture: Manganese Ferrite Nanoparticles as a Micronutrient Fertilizer for Wheat.

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.

Assessment of causal associations between obesity and peripheral artery disease: a bidirectional Mendelian randomization study.

Background: Several observational studies have documented a potential link between obesity and peripheral artery disease (PAD), although conflicting findings exist. The causal relationship between obesity and PAD continues to be a subject of ongoing debate in the medical community. Objectives: In this study, we employed a bidirectional Mendelian randomization (MR) analysis to explore the potential causal relationship between obesity and the risk of PAD. Methods: To investigate these causal relationships, we conducted bidirectional MR analysis using publicly available genome-wide association study (GWAS) data. Effect estimates were calculated using the random-effects inverse variance-weighted (IVW) method. Results: We identified eight independent single nucleotide polymorphisms (SNPs) associated with obesity in 218,735 samples involving 16,380,465 SNPs, all of which met the genome-wide significance threshold (p < 5 × 10-8). The IVW analysis indicates a significant positive association between genetic obesity and multiple datasets with PAD as the outcome: Queue-1 (GWAS ID: finn-b-I9_PAD) (OR = 1.138, 95% CI: 1.027-1.261, p = 0.013), Queue-2 (GWAS ID: bbj-a-144) (OR = 1.190, 95% CI: 1.019-1.390, p = 0.028), Queue-3 (GWAS ID: ebi-a-GCST90018670) (OR = 1.174, 95% CI: 1.014-1.360, p = 0.032), and Queue-4 (GWAS ID: ebi-a-GCST90018890) (OR = 1.194, 95% CI: 1.099-1.296, p < 0.001). However, we did not observe a significant genetic-level association between obesity and PAD for Queue-5 (GWAS ID: ukb-d-I9_PAD) (OR = 1.001, 95% CI: 1.000-1.002, p = 0.071). Furthermore, we conducted a reverse causal MR analysis to explore the potential reverse causal relationship between obesity and PAD. This comprehensive analysis did not provide evidence of a reverse causal association between these two factors. Conclusions: In summary, our study offers genetic evidence suggesting a possible causal link between obesity and PAD. While we did not find evidence supporting the "obesity paradox", prudent weight management remains crucial, as lower weight does not necessarily guarantee better outcomes. As with any study, caution is required in interpreting the findings. Further research is essential to assess the clinical relevance of weight in preventing PAD, which could inform the development of more precise intervention strategies.

Exploring the potential causal relationship between gut microbiota and heart failure: A two-sample mendelian randomization study combined with the geo database.

OBJECTIVE: In recent years, researchers have observed a potential association between alterations in gut microbiota and the onset and progression of heart failure. Nevertheless, the causal relationship between gut microbiota and heart failure remains a topic of controversy. This study employed a two-sample Mendelian randomization approach to investigate the causal link between gut microbiota and heart failure. METHOD: We extracted single nucleotide polymorphism (SNPs) data for heart failure (ebi-a-gcst009541) and gut microbiota from the publicly available genome-wide association analysis (GWAS) summary database. The primary analytical method employed was inverse variance weighting (IVW), complemented by validation using MR-PRESSO, weighted median, and MR pleiotropic residual methods. Additionally, gene pleiotropy (MR-Egger), heterogeneity testing, and a "leave-one-out" analysis were conducted to assess the robustness of the findings. Utilizing the limma package, differentially expressed genes (DEGs) from the Gut Microbiota datasets (GSE3586, GSE5406) and Heart Failure datasets (GSE47908, GSE87466) sourced from the Gene Expression Omnibus (GEO) were curated. Subsequent enrichment analysis was conducted using the Cluster Profiler and GO plot packages to validate the MR analysis outcomes. RESULTS: The results of our analysis revealed seven distinct bacterial groups in the intestines that exhibited associations.with.the.risk.of.heart.failure. These.included.class.negativicutes (P = 0.02,OR:1.11,95%CI:1.02,1.21), gene.eubacterium.eligensgroup (P = 0.02,OR:1.10,95%CI:1.01,1.20),gene.eubacteriummoxidoreducensgroup (P = 0.01,OR:1.10,95%CI:1.02,1.19),Order.selenium (P = 0.02,OR:1.11,95%CI:1.02,1.21), gene.familyxiiiucg001 (P = 0.03,OR=1.09.95%CI:1.01,1.19), gene.familyxiiiad3011group (P = 0.03,OR:0.92,95%CI:0.86,0.99), and.gene.anaerostipes (P = 0.00,OR:0.87,95%CI:0.80,0.94). Nevertheless, upon conducting reverse causal MR analysis, no evidence of a causal relationship between heart failure and the aforementioned seven gut microbiota groups was found.Bioinformatics analysis reveals shared DEGs between gut microbiota and heart failure. CONCLUSION: This Mendelian randomization study represents the first endeavor to explore the causal relationship between specific gut microbiota and heart failure. The findings suggest a significant correlation between these seven specific gut microbiota groups and the risk of heart failure, potentially offering valuable insights for heart failure prevention and control efforts.