Label-free white blood cells classification using a deep feature fusion neural network.

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.

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.

Varietal responses to a soil amendment: Balancing cadmium mitigation and mineral biofortification in wheat production.

The application of soil amendment (SA) and the cultivation of low Cd-accumulating varieties have been a widely favored strategy to enable the safe utilization of Cd-contaminated arable land. However, little has been reported on the reciprocal effects of SA on the Cd mitigation and nutritional quality of different wheat varieties. In this study, we evaluated the impact of an SA on agronomic traits, Cd accumulation, translocation and mineral nutrition of 12 wheat varieties in an acidic field with a Cd concentration of 0.46 mg/kg. The results showed that the SA significantly reduced soil DTPA Cd (42.3 %) and resulted in a slight decrease in wheat grain yield (4.24-9.72 %, average 7.62 %). Similarly, the SA significantly reduced grain Cd concentrations (average 61.65 %) while increased the concentrations of beneficial elements such as Mo and Se in all wheat varieties. However, this intervention also led to a reduction in the concentration of essential mineral elements (such as Ca, Fe, and Mn) in whole wheat grain and starchy endosperm, as well as a reduction in their proportion in the bran. Based on genotypic differences, Huaimai 33, Zhenmai 168, Sumai 188 and Yangmai 28 were considered to be the relatively most promising wheat varieties for achieving a balance among food safety, nutritional quality, and economic yield in this region. Taken together, this study highlights the varietal differences in Cd mitigation and mineral accumulation in different wheat varieties in response to the SA, offering new perspectives for phytoremediation and biofortification strategies for Cd-contaminated farmland.

Foliar application of plant growth regulators for enhancing heavy metal phytoextraction efficiency by Sedum alfredii Hance in contaminated soils: Lab to field experiments.

The phytoremediation efficiency of plants in removing the heavy metals (HMs) might be influenced by their growth status and accumulation capacity of plants. Herein, we conducted a lab-scale experiment and a field try out to assess the optimal plant growth regulators (PGRs) including indole-3-acetic acid (IAA)/brassinolide (BR)/abscisic acid (ABA) in improving the phytoextraction potential of Sedum alfredii Hance (S. alfredii). The results of pot experiment revealed that application of IAA at 0.2 mg/L, BR at 0.4 mg/L, and ABA at 0.2 mg/L demonstrated notable potential as optimal dosage for Cd/Pb/Zn phytoextraction in S. alfredii. The findings of subcellular level of Cd/Pb/Zn in leaves showed that IAA (0.2 mg/L), BR (0.4 mg/L) or ABA (0.2 mg/L) promoted the HMs storage in the soluble and cell wall fraction, therefore contributing HMs subcellular compartmentation. In addition, application of PGRs notably enhanced the antioxidant system (SOD, CAT, POD, APX activities) while reducing lipid peroxidation (MDA content) in S. alfredii, consequently improving HMs tolerance and growth of S. alfredii. Moreover, the results of field trial showed that application of BR, IAA, or ABA+BR substantially improved the growth of S. alfredii by inducing plants biomass and augmenting the levels of photosynthetic pigment contents. Notably, ABA+BR noticed the highest theoretical biomass by 42.9 %, followed by IAA (41.6 %), and BR (36.4 %), as compared with CK. Additionally, ABA+BR treatment showed effectiveness in removing the Cd by 103.4 %, while BR and IAA led to a significant increase of Pb and Zn removal by 239 % and 116 %, respectively, when compared with CK. Overall, the results of this study highlights that the foliar application of IAA, BR, or ABA+BR can serve as viable strategy to boosting phytoremediation efficiency of S. alfredii in contaminated soil by improving the biomass and metal accumulation in harvestable parts.

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.

Efficacy and safety of different traditional Chinese health exercises in patients with coronary heart disease combined with chronic heart failure: A network meta-analysis.

BACKGROUND: Non-pharmacological treatments, particularly TCM health exercises, have garnered attention for their affordability, ease of access, and potential health advantages. Despite this interest, systematic and direct comparative studies assessing the effectiveness and safety of these therapies in patients with CHD-CHF remain scarce. METHODS: This study aimed to compare the efficacy and safety of conventional treatment, conventional treatment integrated with aerobic endurance training, and various TCM health exercises in treating patients with CHD-CHF using NMA. The analysis was designed to provide a reference for developing treatment plans. To achieve this, literature databases were searched for RCTs on different TCM health exercises for CHD-CHF patients up to December 6, 2022. Major outcomes analyzed included NT-proBNP, LVEF, 6-minute walk test, MLHFQ, clinical effectiveness, and adverse event occurrence. The Cochrane risk of bias tool was employed to assess the risk of bias in the included RCT studies. Systematic review with NMA was conducted using RevMan 5.4 and Stata for cumulative ranking, and comparative adjustment funnel plot analysis. RESULTS: Traditional Chinese medicine gong methods included BaDuanJin (A) and TaiChiQuan (B). The NMA and SUCRA results revealed that: A + D and A + C + D were most likely to be the best interventions to improve NT-proBNP; B + D and A + C + D were most likely to be the best interventions to improve LVEF; A + D and A + C + D were the best interventions to improve 6WMT in CHD-CHF patients; B + C + D had the best effect on shrinking LVESD;A + D and B + C + D was likely the best interventions for contracting LVEDD;B + D and A + D were consistent in their capacity to improve MLHFQ in patients with CHD-CHF, but B + D had better efficacy. Unlike A + C + D, B + C + D was the best intervention to improve MLHFQ. In contrast with interventions, including Dand C + D, B + D was the most clinically effective intervention. Unlike interventions including B + C + D, C + D, and D, A + C + D was the most clinically efficient intervention. CONCLUSION: The findings of this NMA showed that traditional Chinese health exercises integrated with conventional treatment are more effective than conventional treatment (D) alone in patients with CHD-CHF, with A + D, B + D, B + C + D, and A + C + D considered potentially optimal treatment interventions.

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.

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.

Zinc glycerolate (Glyzinc): A novel foliar fertilizer for zinc biofortification and cadmium reduction in wheat (Triticum aestivum L.).

Sustainable strategies are essential for zinc (Zn) biofortification and cadmium (Cd) reduction in staple food crops. Herein, we evaluated the phytotoxicity of Glyzinc under foliar and root application (FA&RA) in a lab-scale experiment, and then investigated its Zn efficiency and Cd reduction through foliar application on wheat (Triticum aestivum L.) under field conditions. Compared to RA, FA of Glyzinc exhibited no adverse effect on wheat growth and oxidative stresses at all doses. In field conditions, FA of Glyzinc remarkably increased Zn (28.7 %), S (10.4 %), Cu (17.3 %) and crude protein (9.1 %) content in wheat grain at 100 mg/L without damaging wheat yield. Furthermore, FA of Glyzinc significantly reduced the grain phytic acid (PA) (23.7 %) and Cd level (19.5 %), as well as PA to Zn molar ratio (32.3 %). Overall, our results indicate that Glyzinc has great potential as a high-efficiency foliar fertilizer for Zn biofortification and safe crop production in nano-enabled agriculture.

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.

Microfluidic Particle Separation and Detection System Based on Standing Surface Acoustic Wave and Lensless Imaging.

OBJECTIVE: Separation and detection of micro-particles or cells from bio-samples by point-of-care (POC) systems are critical for biomedical and healthcare diagnostic applications. Among various microfluidic separation techniques, acoustophoresis-based technique has the advantages of label-free and good biocompatibility. However, most of the existing separation techniques are bulky and require additional equipment for analysis. METHODS: We proposed a platform, which integrates an acoustophoresis-based separation device and a lensless imaging sensor into a compact standalone system to tackle this challenge. Standing Surface Acoustic Wave (SSAW) is utilized for label-free particle separation, while lensless imaging is employed for seamless particle detection and counting using self-developed dual-threshold motion detection algorithms. In particular, we specially optimized the design of microfluidic channel and interdigital transducers (IDTs) for higher performance bioparticle separation, designed a heat dissipation system for the suppression of fluid temperature, and proposed a novel frequency-temperature-curve based method to determine the appropriate signal driving frequency for IDTs. RESULTS: At 2 µL/min flow rate, separation efficiency of 93.52% and purity of 94.29% for 15 µm microbead were achieved in mixed 5µm and 15µm microbead solution at a 25 dBm RF driving power, and similar results for mixed 10 µm and 15 µm microbead solution. CONCLUSIONS: The results showed that the integrated platform has an excellent capability to seamlessly separate, distinguish, and count microbeads of different sizes. SIGNIFICANCE: Such a platform and the design methodologies offer a promising POC solution for label-free cell separation and detection in biomedical diagnostics.

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 [...].

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.

High-Efficiency Classification of White Blood Cells Based on Object Detection.

White blood cells (WBCs) play a significant role in the human immune system, and the content of various subtypes of WBCs is usually maintained within a certain range in the human body, while deviant levels are important warning signs for diseases. Hence, the detection and classification of WBCs is an essential diagnostic technique. However, traditional WBC classification technologies based on image processing usually need to segment the collected target cell images from the background. This preprocessing operation not only increases the workload but also heavily affects the classification quality and efficiency. Therefore, we proposed one high-efficiency object detection technology that combines the segmentation and recognition of targets into one step to realize the detection and classification of WBCs in an image at the same time. Two state-of-the-art object detection models, Faster RCNN and Yolov4, were employed and comparatively studied to classify neutrophils, eosinophils, monocytes, and lymphocytes on a balanced and enhanced Blood Cell Count Dataset (BCCD). Our experimental results showed that the Faster RCNN and Yolov4 based deep transfer learning models achieved classification accuracy rates of 96.25% and 95.75%, respectively. For the one-stage model, Yolov4, while ensuring more than 95% accuracy, its detection speed could reach 60 FPS, which showed better performance compared with the two-stage model, Faster RCNN. The high-efficiency object detection network that does not require cell presegmentation can remove the difficulty of image preprocessing and greatly improve the efficiency of the entire classification task, which provides a potential solution for future real-time point-of-care diagnostic systems.

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).

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.

Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients.

Due to the rapid spread of COVID-19 and its induced death worldwide, it is imperative to develop a reliable tool for the early detection of this disease. Chest X-ray is currently accepted to be one of the reliable means for such a detection purpose. However, most of the available methods utilize large training data, and there is a need for improvement in the detection accuracy due to the limited boundary segment of the acquired images for symptom identifications. In this study, a robust and efficient method based on transfer learning techniques is proposed to identify normal and COVID-19 patients by employing small training data. Transfer learning builds accurate models in a timesaving way. First, data augmentation was performed to help the network for memorization of image details. Next, five state-of-the-art transfer learning models, AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, and Xception, with three optimizers, Adam, SGDM, and RMSProp, were implemented at various learning rates, 1e-4, 2e-4, 3e-4, and 4e-4, to reduce the probability of overfitting. All the experiments were performed on publicly available datasets with several analytical measurements attained after execution with a 10-fold cross-validation method. The results suggest that MobileNetv2 with Adam optimizer at a learning rate of 3e-4 provides an average accuracy, recall, precision, and F-score of 97%, 96.5%, 97.5%, and 97%, respectively, which are higher than those of all other combinations. The proposed method is competitive with the available literature, demonstrating that it could be used for the early detection of COVID-19 patients.