Detection of cells by flow cytometry:Counting,imaging,and cell classification

The study of circulating cells in the blood stream is critical,as it covers many felds of biomed-icine,including immunology,cell biology,oncology,and reproductive medicine.In-viuo flowcytometry(IVFC)is a new tool to monitor and count cells in real time for long durations in theirnative biological environment.This review describes two main categories of IVFC,ie.,labeledand label-free IVFC.It focuses on label-free IVFC and introduces its technological developmentand related biological applications.Because cell recognition is the basis of flow cytometrycounting,this review also describes various methods for the classification of unlabeled cells,including the latest machine learning-based technologies.

Impact of Power Control Strategy and Cell Classification on Hierarchical Cellular System's Reverse Link

This contribution deals with the outage probability in a hierarchical macrocell/microcell CDMA cellularsystem.We consider different attenuation models and imperfection of power control with log-normal distribution.Based on IS-95 protocol, the impacts of imperfect sectorization and imperfection of power control on outageProbability are fully investigated From the numerical results, we conclude that the high user capacity may beexpected in the case of relatively tight power control and narrower overlap betWeen sectors and the hierarchicalmacrocell/microcell cellular systems are potential for the future cellular mobile communication.

A pilot allocation method for multi-cell multi-user massive MIMO system

Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output(MIMO)systems.This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency.The pilots of the terminals are allocated onebit orthogonal identifier to diminish the cell categories by the operation of exclusive OR(XOR).At the same time,the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm,and different pilot sequences are assigned to different groups.The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.

Polarity-triggered anti-Kasha system for high-contrast cell imaging and classification

Kasha’s rule,which states that all exciton emissions occur from the lowest excited state and are independent of excitation energy,makes high-energy excitons difficult to use and severely hinders the widespread applications of organic photoluminescent materials in the real world.For decades,scientists have tried to break this rule to unleash the power of high-energy excitons,but only minimal progress has been achieved,with no rational guiding principles provided,and few applications developed.So far,breaking Kasha’s rule has remained a purely academic concept.In this paper,we introduce a design principle for a purely organic anti-Kasha system and synthesise a series of compounds based on the design rule.As predicted,these compounds all display evident S_(2) emissions in dilute solutions.In addition,we introduce a highly accurate(over 90%)convolutional neural network as an assistant for the classification of cells using anti-Kasha luminogens,thereby providing a new application direction for anti-Kasha systems.

A counting method for complex overlapping erythrocytes-based microscopic imaging

Red blood cell(RBC)counting is a standard medical test that can help diagnose various conditions and diseases.Manual counting of blood cells is highly tedious and time consuming.However,new methods for counting blood cells are customary employing both electronic and computer-assisted techniques.Image segmentation is a classical task in most image processing applications which can be used to count blood cells in a microscopic image.In this research work,an approach for erythrocytes counting is proposed.We employed a classification before counting and a new segmentation idea was implemented on the complex overlapping clusters in a microscopic smear image.Experimental results show that the proposed method is of higher counting accuracy and it performs much better than most counting algorithms existed in the situation of three or more RBCs overlapping complexly into a group.The average total erythrocytes counting accuracy of the proposed method reaches 92.9%.

Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy

We present a deep learning approach for living cells mitosis classification based on label-free quantitative phase imaging with transport of intensity equation methods.In the approach,we applied a pretrained deep convolutional neural network using transfer learning for binary classification of mitosis and non-mitosis.As a validation,we demonstrated the performances of the network trained by phase images and intensity images,respectively.The convolutional neural network trained by phase images achieved an average accuracy of 98.9%on the validation data,which outperforms the average accuracy 89.6%obtained by the network trained by intensity images.We believe that the quantitative phase microscopy in combination with deep learning enables researchers to predict the mitotic status of living cells noninvasively and efficiently.

DNA Framework-based Topological Aptamer for Differentiating Subtypes of Hepatocellular Carcinoma Cells

Hepatocellular carcinoma(HCC) remains a global health challenge with a growing incidence worldwide. The accurate identification of liver HCC cell subtypes plays crucial roles in precision medicine and prognosis. Nevertheless, simple and efficient methods for cell subtype discrimination still remain an issue to be studied. In this study, we construct topological probes by using a tetrahedral DNA framework(TDF) to topologically engineer the spatial orientations of the aptamers. The three vertexes of a TDF were algebraic topologically anchored with aptamers targeting epithelial cell adhesion molecule(EpCAM), which may express differently on different subtypes of HCC cells. Using the TDF-based topological aptamer(TDF-TA), we accomplish the differentiation of HCC cell subtypes, including high-metastatic, low-metastatic HCC and normal cells based on flow cytometry(FCM) and fluorescence microscope imaging. By replacing the fluorescent indicator modified on aptamers with photoacoustic dyes, we achieve the discrimination of different HCC cells using photoacoustic imaging technology, further demonstrating the feasibility of the TDF-based topological probe for HCC cell subtype discrimination. This TDF-based topological engineering strategy thus provides a flexible means for subtype cell discrimination, which may provide new ideas for achieving accurate diagnosis of HCC.