scholarly journals Artificial  Intelligence Enabled Reagent-free Imaging Hematology Analyzer            

2021 ◽  
Author(s):  
Xin Shu ◽  
Sameera Sansare ◽  
Di Jin ◽  
Xiangxiang Zhang ◽  
Kai-Yu Tong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Diseases ◽  
Low Cost ◽  
Rapid Screening ◽  
Label Free ◽  
Cd8 Cells ◽  
Quantitative Phase ◽  
Minimal Sample ◽  
Hematology Analyzer ◽  
Hematology Analyzers

Leukocyte differential test is a widely performed clinical procedure for screening infectious diseases. Existing hematology analyzers require labor-intensive work and a panel of expensive reagents. Here we report an artificial-intelligence enabled reagent-free imaging hematology analyzer (AIRFIHA) modality that can accurately classify subpopulations of leukocytes with minimal sample preparation. AIRFIHA is realized through training a two-step residual neural network using label-free images of isolated leukocytes acquired from a custom-built quantitative phase microscope. By leveraging the rich information contained in quantitative phase images, we not only achieved high accuracy in differentiating B and T lymphocytes, but also classified CD4 and CD8 cells, therefore outperforming the classification accuracy of most current hematology analyzers. We validated the performance of AIRFIHA in a randomly selected test set and cross-validated it across all blood donors. Owing to its easy operation, low cost, and accurate discerning capability of complex leukocyte subpopulations, we envision AIRFIHA is clinically translatable and can also be deployed in resource-limited settings, e.g., during pandemic situations for the rapid screening of infectious diseases. Corresponding author(s) Email:    [email protected],  [email protected]

scholarly journals Rapid and label-free identification of individual bacterial pathogens exploiting three-dimensional quantitative phase imaging and deep learning

2019 ◽  
Author(s):  
Geon Kim ◽  
Daewoong Ahn ◽  
Minhee Kang ◽  
YoungJu Jo ◽  
Donghun Ryu ◽  
...  
Keyword(s):  
Refractive Index ◽  
Deep Learning ◽  
Infectious Diseases ◽  
Bacterial Pathogens ◽  
Bacterial Species ◽  
Three Dimensional ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

ABSTRACTFor appropriate treatments of infectious diseases, rapid identification of the pathogens is crucial. Here, we developed a rapid and label-free method for identifying common bacterial pathogens as individual bacteria by using three-dimensional quantitative phase imaging and deep learning. We achieved 95% accuracy in classifying 19 bacterial species by exploiting the rich information in three-dimensional refractive index tomograms with a convolutional neural network classifier. Extensive analysis of the features extracted by the trained classifier was carried out, which supported that our classifier is capable of learning species-dependent characteristics. We also confirmed that utilizing three-dimensional refractive index tomograms was crucial for identification ability compared to two-dimensional imaging. This method, which does not require time-consuming culture, shows high feasibility for diagnosing patients with infectious diseases who would benefit from immediate and adequate antibiotic treatment.

scholarly journals Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Arun Sharma ◽  
Sheeba Rani ◽  
Dinesh Gupta
Keyword(s):  
Artificial Intelligence ◽  
Infectious Diseases ◽  
Transfer Learning ◽  
Rapid Screening ◽  
Learning Approach ◽  
Classification Models ◽  
Classification Problems ◽  
X Ray ◽  
Healthcare Crisis ◽  
Chest X Ray

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.

scholarly journals Platforms for Production of Protein-Based Vaccines: From Classical to Next-Generation Strategies

Biomolecules ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1072
Author(s):  
Raquel Cid ◽  
Jorge Bolívar
Keyword(s):  
Infectious Diseases ◽  
Mammalian Cells ◽  
Vaccine Development ◽  
Low Cost ◽  
Cost Effective ◽  
Insect Larvae ◽  
Subunit Vaccines ◽  
Effective Strategies ◽  
Alternative Systems ◽  
Inactivated Vaccines

To date, vaccination has become one of the most effective strategies to control and reduce infectious diseases, preventing millions of deaths worldwide. The earliest vaccines were developed as live-attenuated or inactivated pathogens, and, although they still represent the most extended human vaccine types, they also face some issues, such as the potential to revert to a pathogenic form of live-attenuated formulations or the weaker immune response associated with inactivated vaccines. Advances in genetic engineering have enabled improvements in vaccine design and strategies, such as recombinant subunit vaccines, have emerged, expanding the number of diseases that can be prevented. Moreover, antigen display systems such as VLPs or those designed by nanotechnology have improved the efficacy of subunit vaccines. Platforms for the production of recombinant vaccines have also evolved from the first hosts, Escherichia coli and Saccharomyces cerevisiae, to insect or mammalian cells. Traditional bacterial and yeast systems have been improved by engineering and new systems based on plants or insect larvae have emerged as alternative, low-cost platforms. Vaccine development is still time-consuming and costly, and alternative systems that can offer cost-effective and faster processes are demanding to address infectious diseases that still do not have a treatment and to face possible future pandemics.

scholarly journals Demonstration of a Label-Free and Low-Cost Optical Cavity-Based Biosensor Using Streptavidin and C-Reactive Protein

Biosensors ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 4
Author(s):  
Donggee Rho ◽  
Seunghyun Kim
Keyword(s):  
Limit Of Detection ◽  
Point Of Care ◽  
Low Cost ◽  
Optical Cavity ◽  
Sample Volume ◽  
Small Sample ◽  
Label Free ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Cavity Structure

An optical cavity-based biosensor (OCB) has been developed for point-of-care (POC) applications. This label-free biosensor employs low-cost components and simple fabrication processes to lower the overall cost while achieving high sensitivity using a differential detection method. To experimentally demonstrate its limit of detection (LOD), we conducted biosensing experiments with streptavidin and C-reactive protein (CRP). The optical cavity structure was optimized further for better sensitivity and easier fluid control. We utilized the polymer swelling property to fine-tune the optical cavity width, which significantly improved the success rate to produce measurable samples. Four different concentrations of streptavidin were tested in triplicate, and the LOD of the OCB was determined to be 1.35 nM. The OCB also successfully detected three different concentrations of human CRP using biotinylated CRP antibody. The LOD for CRP detection was 377 pM. All measurements were done using a small sample volume of 15 µL within 30 min. By reducing the sensing area, improving the functionalization and passivation processes, and increasing the sample volume, the LOD of the OCB are estimated to be reduced further to the femto-molar range. Overall, the demonstrated capability of the OCB in the present work shows great potential to be used as a promising POC biosensor.

scholarly journals Integrating high-performing electrochemical transducers in lateral flow assay

2021 ◽  
Author(s):  
Antonia Perju ◽  
Nongnoot Wongkaew
Keyword(s):  
High Performance ◽  
Point Of Care ◽  
Low Cost ◽  
High Sensitivity ◽  
Lateral Flow ◽  
Analytical Performance ◽  
Label Free ◽  
High Performing ◽  
Lateral Flow Assays ◽  
Electrochemical Transducers

AbstractLateral flow assays (LFAs) are the best-performing and best-known point-of-care tests worldwide. Over the last decade, they have experienced an increasing interest by researchers towards improving their analytical performance while maintaining their robust assay platform. Commercially, visual and optical detection strategies dominate, but it is especially the research on integrating electrochemical (EC) approaches that may have a chance to significantly improve an LFA’s performance that is needed in order to detect analytes reliably at lower concentrations than currently possible. In fact, EC-LFAs offer advantages in terms of quantitative determination, low-cost, high sensitivity, and even simple, label-free strategies. Here, the various configurations of EC-LFAs published are summarized and critically evaluated. In short, most of them rely on applying conventional transducers, e.g., screen-printed electrode, to ensure reliability of the assay, and additional advances are afforded by the beneficial features of nanomaterials. It is predicted that these will be further implemented in EC-LFAs as high-performance transducers. Considering the low cost of point-of-care devices, it becomes even more important to also identify strategies that efficiently integrate nanomaterials into EC-LFAs in a high-throughput manner while maintaining their favorable analytical performance.

scholarly journals Characterization of Liposomes Using Quantitative Phase Microscopy (QPM)

Pharmaceutics ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 590
Author(s):  
Jennifer Cauzzo ◽  
Nikhil Jayakumar ◽  
Balpreet Singh Ahluwalia ◽  
Azeem Ahmad ◽  
Nataša Škalko-Basnet
Keyword(s):  
Rapid Development ◽  
Fluorescent Labeling ◽  
Label Free ◽  
Batch Mode ◽  
Full Field ◽  
Quantitative Phase ◽  
Phase Microscopy ◽  
Quantitative Phase Microscopy ◽  
Tracking Ability

The rapid development of nanomedicine and drug delivery systems calls for new and effective characterization techniques that can accurately characterize both the properties and the behavior of nanosystems. Standard methods such as dynamic light scattering (DLS) and fluorescent-based assays present challenges in terms of system’s instability, machine sensitivity, and loss of tracking ability, among others. In this study, we explore some of the downsides of batch-mode analyses and fluorescent labeling, while introducing quantitative phase microscopy (QPM) as a label-free complimentary characterization technique. Liposomes were used as a model nanocarrier for their therapeutic relevance and structural versatility. A successful immobilization of liposomes in a non-dried setup allowed for static imaging conditions in an off-axis phase microscope. Image reconstruction was then performed with a phase-shifting algorithm providing high spatial resolution. Our results show the potential of QPM to localize subdiffraction-limited liposomes, estimate their size, and track their integrity over time. Moreover, QPM full-field-of-view images enable the estimation of a single-particle-based size distribution, providing an alternative to the batch mode approach. QPM thus overcomes some of the drawbacks of the conventional methods, serving as a relevant complimentary technique in the characterization of nanosystems.

scholarly journals Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yu-An Chiou ◽  
Jhen-Yang Syu ◽  
Sz-Ying Wu ◽  
Lian-Yu Lin ◽  
Li Tzu Yi ◽  
...  
Keyword(s):  
Heart Failure ◽  
Low Cost ◽  
Screening Method ◽  
Systolic Heart Failure ◽  
Rapid Screening ◽  
Ecg Signals ◽  
Testing Dataset ◽  
Lead Selection ◽  
Ecg Leads ◽  
Intelligent Screening

AbstractElectrocardiogram (ECG)-based intelligent screening for systolic heart failure (HF) is an emerging method that could become a low-cost and rapid screening tool for early diagnosis of the disease before the comprehensive echocardiographic procedure. We collected 12-lead ECG signals from 900 systolic HF patients (ejection fraction, EF < 50%) and 900 individuals with normal EF in the absence of HF symptoms. The 12-lead ECG signals were converted by continuous wavelet transform (CWT) to 2D spectra and classified using a 2D convolutional neural network (CNN). The 2D CWT spectra of 12-lead ECG signals were trained separately in 12 identical 2D-CNN models. The 12-lead classification results of the 2D-CNN model revealed that Lead V6 had the highest accuracy (0.93), sensitivity (0.97), specificity (0.89), and f1 scores (0.94) in the testing dataset. We designed four comprehensive scoring methods to integrate the 12-lead classification results into a key diagnostic index. The highest quality result among these four methods was obtained when Leads V5 and V6 of the 12-lead ECG signals were combined. Our new 12-lead ECG signal–based intelligent screening method using straightforward combination of ECG leads provides a fast and accurate approach for pre-screening for systolic HF.

scholarly journals mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Artur Yakimovich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Edge Label ◽  
Bacterial Colony ◽  
Link Type ◽  
Colony Counting ◽  
Anthrax Spores ◽  
Infection Biology

ABSTRACT Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers “Holographic Deep Learning for Rapid Optical Screening of Anthrax Spores” by Jo et al. (Y. Jo, S. Park, J. Jung, J. Yoon, et al., Sci Adv 3:e1700606, 2017, https://doi.org/10.1126/sciadv.1700606) and “Bacterial Colony Counting with Convolutional Neural Networks in Digital Microbiology Imaging” by Ferrari and colleagues (A. Ferrari, S. Lombardi, and A. Signoroni, Pattern Recognition 61:629–640, 2017, https://doi.org/10.1016/j.patcog.2016.07.016). Here he discusses how these papers made an impact on him by showcasing that artificial intelligence algorithms can be equally applicable to both classical infection biology techniques and cutting-edge label-free imaging of pathogens.

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