scholarly journals Intelligent classification of platelet aggregates by agonist type

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Yuqi Zhou ◽  
Atsushi Yasumoto ◽  
Cheng Lei ◽  
Chun-Jung Huang ◽  
Hirofumi Kobayashi ◽  
...  
Keyword(s):  
Cancer Metastasis ◽  
Underlying Mechanism ◽  
Clinical Diagnostics ◽  
New Class ◽  
Imaging Flow Cytometry ◽  
Different Types ◽  
Platelet Aggregates ◽  
High Throughput Imaging ◽  
Aggregation Of Platelets

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

scholarly journals Classification of platelet aggregates by agonist type

2019 ◽  
Author(s):  
Yuqi Zhou ◽  
Atsushi Yasumoto ◽  
Cheng Lei ◽  
Chun-Jung Huang ◽  
Hirofumi Kobayashi ◽  
...  
Keyword(s):  
Cancer Metastasis ◽  
Underlying Mechanism ◽  
Clinical Diagnostics ◽  
New Class ◽  
Imaging Flow Cytometry ◽  
Different Types ◽  
Platelet Aggregates ◽  
High Throughput Imaging ◽  
Aggregation Of Platelets

SUMMARYPlatelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

Classification of platelet aggregates by intelligent imaging flow cytometry (Conference Presentation)

2020 ◽  
Author(s):  
Yuqi Zhou ◽  
Atsushi Yasumoto ◽  
Cheng Lei ◽  
Chun-Jung Huang ◽  
Hirofumi Kobayashi ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Imaging Flow Cytometry ◽  
Platelet Aggregates

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations

1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe
Keyword(s):  
Explanatory Power ◽  
Prototype System ◽  
Inference Rules ◽  
Medical Expert ◽  
Comprehensive Knowledge ◽  
Different Types ◽  
Medical Expert Systems ◽  
Explanation System ◽  
Systems Studies

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

scholarly journals PTPRD-inactivation-induced CXCL8 promotes angiogenesis and metastasis in gastric cancer and is inhibited by metformin

2019 ◽  
Vol 38 (1) ◽  
Author(s):  
Won Jung Bae ◽  
Ji Mi Ahn ◽  
Hye Eun Byeon ◽  
Seokwhi Kim ◽  
Dakeun Lee
Keyword(s):  
Microarray Analysis ◽  
Protein Tyrosine Phosphatase ◽  
Cancer Metastasis ◽  
Tyrosine Phosphatase ◽  
Underlying Mechanism ◽  
Tube Formation ◽  
Potential Therapeutic Target ◽  
Efficient Treatment ◽  
Stat3 Signaling

Abstract Background Protein tyrosine phosphatase receptor delta (PTPRD) is frequently inactivated in various types of cancers. Here, we explored the underlying mechanism of PTPRD-loss-induced cancer metastasis and investigated an efficient treatment option for PTPRD-inactivated gastric cancers (GCs). Methods PTPRD expression was evaluated by immunohistochemistry. Microarray analysis was used to identify differentially expressed genes in PTPRD-inactivated cancer cells. Quantitative reverse transcription (qRT-PCR), western blotting, and/or enzyme-linked immunosorbent assays were used to investigate the PTPRD-CXCL8 axis and the expression of other related genes. An in vitro tube formation assay was performed using HUVECs. The efficacy of metformin was assessed by MTS assay. Results PTPRD was frequently downregulated in GCs and the loss of PTPRD expression was associated with advanced stage, worse overall survival, and a higher risk of distant metastasis. Microarray analysis revealed a significant increase in CXCL8 expression upon loss of PTPRD. This was validated in various GC cell lines using transient and stable PTPRD knockdown. PTPRD-loss-induced angiogenesis was mediated by CXCL8, and the increase in CXCL8 expression was mediated by both ERK and STAT3 signaling. Thus, specific inhibitors targeting ERK or STAT3 abrogated the corresponding signaling nodes and inhibited PTPRD-loss-induced angiogenesis. Additionally, metformin was found to efficiently inhibit PTPRD-loss-induced angiogenesis, decrease cell viability in PTPRD-inactivated cancers, and reverse the decrease in PTPRD expression. Conclusions Thus, the PTPRD-CXCL8 axis may serve as a potential therapeutic target, particularly for the suppression of metastasis in PTPRD-inactivated GCs. Hence, we propose that the therapeutic efficacy of metformin in PTPRD-inactivated cancers should be further investigated.

scholarly journals Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yersultan Mirasbekov ◽  
Adina Zhumakhanova ◽  
Almira Zhantuyakova ◽  
Kuanysh Sarkytbayev ◽  
Dmitry V. Malashenkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Spatial Resolution ◽  
Mesocosm Experiment ◽  
Imaging Flow Cytometry ◽  
Leibler Divergence ◽  
Temporal And Spatial ◽  
High Level ◽  
Training Sets

AbstractA machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Preventing corneal blindness caused by keratitis using artificial intelligence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Jiewei Jiang ◽  
Kuan Chen ◽  
Qianqian Chen ◽  
Qinxiang Zheng ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Vision Loss ◽  
Learning System ◽  
Automated Classification ◽  
Slit Lamp ◽  
Resource Limited ◽  
The World ◽  
Different Types ◽  
Corneal Blindness

AbstractKeratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

scholarly journals Electrospun Nanofibers for Sensing and Biosensing Applications—A Review

2021 ◽  
Vol 22 (12) ◽  
pp. 6357
Author(s):  
Kinga Halicka ◽  
Joanna Cabaj
Keyword(s):  
High Surface ◽  
High Surface Area ◽  
Toxic Metal ◽  
Volume Ratio ◽  
Electrospun Nanofibers ◽  
Clinical Diagnostics ◽  
Loading Capacity ◽  
Electrospun Nanofiber ◽  
Sensing Platforms ◽  
Different Types

Sensors and biosensors have found applications in many areas, e.g., in medicine and clinical diagnostics, or in environmental monitoring. To expand this field, nanotechnology has been employed in the construction of sensing platforms. Because of their properties, such as high surface area to volume ratio, nanofibers (NFs) have been studied and used to develop sensors with higher loading capacity, better sensitivity, and faster response time. They also allow to miniaturize designed platforms. One of the most commonly used techniques of the fabrication of NFs is electrospinning. Electrospun NFs can be used in different types of sensors and biosensors. This review presents recent studies concerning electrospun nanofiber-based electrochemical and optical sensing platforms for the detection of various medically and environmentally relevant compounds, including glucose, drugs, microorganisms, and toxic metal ions.

NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 06 (01) ◽  
pp. 61-75 ◽  
Author(s):  
R. PANCHAL ◽  
B. VERMA
Keyword(s):  
Breast Cancer ◽  
Research Work ◽  
Breast Cancer Diagnosis ◽  
Classification Rate ◽  
Test Dataset ◽  
Benchmark Database ◽  
Different Types ◽  
Breast Tissues ◽  
Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

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