The traditional diagnosis of leukemia relies on pathologists to observe and classify cells on bone marrow smears, which is low-throughput, time-consuming, and subject to human bias. To overcome these limitations, we demonstrate intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging (OTS-QPI) that acquires bright-field and quantitative phase images of white blood cells (WBCs) containing leukemia cells with high throughput (15,000 cells/s) for deep-learning-based classification. After trained with 64,000 images, a convolutional neural network (CNN) distinguishes three different types of leukemia cells from WBCs with an accuracy of over 96%. Our method provides new possibilities for high-throughput, label-free, and intelligent leukemia diagnosis.
We present an extreme-throughput (>1 million cells per second) imaging flow cytometer with deep learning to achieve a highly simple, rapid, and cost-effective liquid biopsy for ex-vivo drug-susceptibility testing of leukemia. The drug resistance of leukemia cells was detected in whole blood with only 24-hour drug treatment without hemolysis or dilution, making the sample preparation extremely simple, rapid and cost-effective. Our method also accurately evaluated the drug susceptibility of white blood cells from untreated patients with acute lymphoblastic leukemia, holding great promise for affordable precision medicine.
Platelets participate in both physiological hemostasis and pathological thrombosis by forming aggregates activated by various agonists. However, it has been considered impossible to identify the stimuli and classify the aggregates. Here we present an intelligent method for classifying platelet aggregates by agonist type based on the combination of high-throughput imaging flow cytometry and a convolutional neural network. It morphologically identifies the contributions of different agonists to platelet aggregation with high accuracy. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to develop a new class of clinical diagnostics and therapeutics.
Although the basic mechanism of ECMO and related treatment have been increasingly mature, there is still a high proportion of complications. Currently, clinicians can only adjust the pattern of ECMO through basic physiological signs, but this method is not good at detecting hemodynamic changes in peripheral tissues, and there is no reliable and immediate evaluation mechanism.
In this study, the ability of subknee blood circulation was evaluated by fNIRS. Monitoring and adjusting blood perfusion volume of peripheral tissues can reduce the incidence of complications and achieve the feasibility and effectiveness of the ability assessment of blood circulation function.
Optofluidic time-stretch microscopy is a powerful tool in imaging flow cytometry as it enables continuous image acquisition at a frame rate higher than 10,000 frames per second. In addition to bright-field imaging that provides morphological information, attempts have been made to integrate quantitative phase imaging (QPI) with optofluidic time-stretch microscopy to acquire information related to subcellular structure, such as the refractive index and thickness. However, the applicability of such methods is hindered by errors introduced during phase unwrapping and the need for a high-bandwidth photodetector. To overcome these limitations, here we demonstrate optofluidic time-stretch QPI based on an acousto-optic modulator (AOM) that acquires intensity and phase image with a low-bandwidth photodetector without phase-unwrapping errors. In our system, the signal beam that carries cellular information interferes with the reference beam, the frequency of which is shifted by 1/4 of the repetition frequency of the laser by an AOM. The beat note is then detected by a normal photodetector, and its waveform that consists of groups of four successive pulses is converted into phase and intensity images with simple calculations. Therefore, we lower the requirement of the photodetector bandwidth and eliminate the errors in phase unwrapping while maintaining a throughput of 10,000 cells per second. These advantages of our system offer new possibilities for high-throughput label-free cancer cell detection in blood by looking at cellular phase information including structural features, enabling early cancer detection and improving the effectiveness of treatment.
While fluorescence imaging flow cytometry is a promising method for high-throughput single-cell analysis, it has not been suitable for analysis of large populations of cells (e.g., blood samples) due to its low imaging sensitivity at a high cell throughput. Here we present fluorescence imaging flow cytometry with an ultrahigh imaging sensitivity, which is enabled by virtual motion freezing. In this method, we prepare a wide-field imaging system with a CMOS camera and scan images of flowing cells by a scanning device, such as a polygon scanner, equipped in the imaging system so that the motion of the cells is canceled in the imaging plane, thus significantly extending the exposure time of the camera without suffering from motion blur. Additionally, we scan a loosely focused excitation beam during the exposure time of the camera in the direction opposite to the cell flow using a beam scanner such as an acousto-optic deflector, which significantly reduces motion cancellation errors caused by the image distortion of the imaging system and hence allows further extension of the exposure time. Consequently, our method improves imaging sensitivity by a factor of ~1,000 compared with a conventional wide-field excitation method, enabling acquisition of microscopy-grade images of fast flowing cells. As a proof-of-concept, we obtained fluorescence images of nuclei of murine white blood cells stained by SYTO16 at a flow speed of 1 m/s (corresponding to a cell throughput of 10,000 cells/s assuming the 100-μm cell spacing) and determined the population of nuclear lobulation from the high-signal-to-noise-ratio images obtained.
Verbal fluency tests (VFT) are widely used neuropsychological tests of frontal lobe and have been frequently used in various functional brain mapping studies. There are two versions of VFT based on the type of cue: the letter fluency task (LFT) and the category fluency task (CFT). However, the fundamental aspect of the brain connectivity across spatial regions of the fronto-temporal regions during the VFTs has not been elucidated to date. In this study we hypothesized that different cortical functional connectivity over bilateral fronto-temporal regions can be observed by means of multi-channel fNIRS in the LFT and the CFT respectively. Our results from fNIRS (ETG-4000) showed different patterns of brain functional connectivity consistent with these different cognitive requirements. We demonstrate more brain functional connectivity over frontal and temporal regions during LFT than CFT, and this was in line with previous brain activity studies using fNIRS demonstrating increased frontal and temporal region activation during LFT and CFT and more pronounced frontal activation by the LFT.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.