scholarly journals Classification of Preeclamptic Placental Extracellular Vesicles Using Femtosecond Laser-fabricated Nanoplasmonic Sensors and Machine Learning

2021 ◽  
Author(s):  
Mohammadrahim Kazemzadeh ◽  
Miguel Martinez-Calderon ◽  
Song Y. Paek ◽  
MoiMoi Lowe ◽  
Claude Aguergaray ◽  
...  

Placental extracellular vesicles (EVs) play an essential role in pregnancy by protecting and transporting diverse biomolecules that aid in fetomaternal communication. However, in preeclampsia, they have also been implicated in contributing to disease progression. Despite their potential clinical value, most current technologies cannot provide a rapid and effective means of differentiating between healthy and diseased placental EVs. To address this, we developed a fabrication process called laser-induced nanostructuring of SERS-active thin films (LINST), which produces nanoplasmonic substrates that provide exceptional Raman signal enhancement and allow the biochemical fingerprinting of EVs. After validating LINST performance with chemical standards, we used placental EVs from tissue explant cultures and demonstrated that preeclamptic and normotensive placental EVs have classifiably distinct Raman spectra following the application of both conventional and advanced machine learning algorithms. Given the abundance of placental EVs in maternal circulation, these findings will encourage immediate exploration of surface-enhanced Raman spectroscopy (SERS) as a promising method for preeclampsia liquid biopsies, while our novel fabrication process can provide a versatile and scalable substrate for many other SERS applications.

2021 ◽  
Author(s):  
Mohammadrahim Kazemzadeh ◽  
Colin Hisey ◽  
Priscila Dauros Singorenko ◽  
Simon Swift ◽  
Kamran Zargar ◽  
...  

Bacterial extracellular vesicles (EVs) are nanoscale lipid-enclosed packages that are released by bacteria cells and shuttle various biomolecules between bacteria or host cells. They are implicated in playing several important roles, from infectious disease progression to maintaining proper gut health, however the tools available to characterise and classify them are limited and impractical for many applications. Surface-enhanced Raman Spectroscopy (SERS) provides a promising means of rapidly fingerprinting bacterial EVs in a label-free manner by taking advantage of plasmonic resonances that occur on nanopatterned surfaces, effectively amplifying the inelastic scattering of incident light. In this study, we demonstrate that by applying machine learning algorithms to bacterial EV SERS spectra, EVs from cultures of the same bacterial species Escherichia coli can be classified by strain, culture conditions, and purification method. While these EVs are highly purified and homogeneous compared to complex samples, the ability to classify them from a single species demonstrates the incredible power of SERS when combined with machine learning, and the importance of considering these parameters in future applications. We anticipate that these findings will play a crucial role in developing the laboratory and clinical utility of bacterial EVs, such as the label-free, noninvasive, and rapid diagnosis of infections without the need to culture samples from blood, urine, or other fluids.<br>


2021 ◽  
Author(s):  
Mohammadrahim Kazemzadeh ◽  
Colin Hisey ◽  
Priscila Dauros Singorenko ◽  
Simon Swift ◽  
Kamran Zargar ◽  
...  

Bacterial extracellular vesicles (EVs) are nanoscale lipid-enclosed packages that are released by bacteria cells and shuttle various biomolecules between bacteria or host cells. They are implicated in playing several important roles, from infectious disease progression to maintaining proper gut health, however the tools available to characterise and classify them are limited and impractical for many applications. Surface-enhanced Raman Spectroscopy (SERS) provides a promising means of rapidly fingerprinting bacterial EVs in a label-free manner by taking advantage of plasmonic resonances that occur on nanopatterned surfaces, effectively amplifying the inelastic scattering of incident light. In this study, we demonstrate that by applying machine learning algorithms to bacterial EV SERS spectra, EVs from cultures of the same bacterial species Escherichia coli can be classified by strain, culture conditions, and purification method. While these EVs are highly purified and homogeneous compared to complex samples, the ability to classify them from a single species demonstrates the incredible power of SERS when combined with machine learning, and the importance of considering these parameters in future applications. We anticipate that these findings will play a crucial role in developing the laboratory and clinical utility of bacterial EVs, such as the label-free, noninvasive, and rapid diagnosis of infections without the need to culture samples from blood, urine, or other fluids.<br>


2021 ◽  
Author(s):  
Mohammadrahim Kazemzadeh ◽  
Colin Hisey ◽  
Kamran Zargar ◽  
Peter Xu ◽  
Neil Broderick

<div>Machine learning has shown great potential for classifying diverse samples in biomedical applications based on their Raman spectra. However, the acquired spectra typically require several preprocessing steps before standard machine learning algorithms can accurately and reliably classify them. To simplify this workflow and enable future growth of this technology, we present a unified solution for classifying biological Raman spectra without any need of prepossessing, including denoising and baseline establishment. This method is developed based on a custom version of a convolutional neural network (CNN) elicited from ResNet architecture, combined with our proposed data augmentation technique. The superiority of this method compared to conventional classification techniques is shown by applying it to Raman spectra of different grades of bladder cancer tissue and surface enhanced Raman spectroscopy (SERS) spectra of various strains of E. Coli extracellular vesicles (EVs). These results show that our method is far more robust compared to its conventional counterparts when dealing with the various kinds of spectral baselines produced by different Raman spectrometers.</div>


2018 ◽  
Vol 24 (9) ◽  
pp. 974-982 ◽  
Author(s):  
Carlos Salomon ◽  
Zarin Nuzhat ◽  
Christopher L. Dixon ◽  
Ramkumar Menon

Parturition is defined as the action or process of giving birth to offspring. Normal term human parturition ensues following the maturation of fetal organ systems typically between 37 and 40 weeks of gestation. Our conventional understanding of how parturition initiation is signaled revolves around feto-maternal immune and endocrine changes occurring in the intrauterine cavity. These changes in turn correlate with the sequence of fetal growth and development. These important physiological changes also result in homeostatic imbalances which result in heightened inflammatory signaling. This disrupts the maintenance of pregnancy, thus leading to laborrelated changes. However, the precise mechanisms of the signaling cascades that lead to the initiation of parturition remain unclear, although exosomes may be a mediator of this process. Exosomes are a subtype of extracellular vesicles characterised by their endocytic origin. This involves the trafficking of intraluminal vesicles into multivesicular bodies (MVB) and then exocytosis via the plasmatic membranes. Exosomes are highly stable nanovesicles that are released by a wide range of cells and organs including the human placenta and fetal membranes. Interestingly, exosomes from placental origin have been uncovered in maternal circulation across gestation. In addition, their concentration is higher in pregnancies with complications such as gestational diabetes and preeclampsia. In normal gestation, the concentration of placental exosomes in maternal circulation correlates with placental weight at third trimester. The role of placental exosomes across gestation has not been fully elucidated, although recent studies suggest that placental exosomes are involved in maternal-fetal inmmuno-tolerance, maternal systemic inflammation and nutrient transport. The content of exosomes is of particular importance, encompassing a large range of molecules such as mRNA, miRNAs, DNA, lipids, cell-surface receptors, and protein mediators. These can in turn interact with either adjacent or distal cells to reprogram their phenotype and regulate their function. Many of the pro-parturition proinflammatory mediators reach maternal compartments from the fetal side via circulation, but major impediments remain, such as degradation at various levels and limited halflife in circulation. Recent findings suggest that a more effective mode of communication and signal transport is through exosomes, where signals are protected and will not succumb to degradation. Thus, understanding how exosomes regulate key events throughout pregnancy and parturition will provide an opportunity to understand the mechanisms involved in the maternal and fetal metabolic adaptations during normal and pathological pregnancies. Subsequently, this will assist in identifying those pregnancies at risk of developing complications. This may also allow more appropriate modifications of their clinical management. This review will hence examine the current body of data to summarise our understanding of how signaling pathways lead to the beginning of parturition. In addition, we propose that extracellular vesicles, namely exosomes, may be an integral component of these signaling events by transporting specific signals to prepare the maternal physiology to initiate parturition. Understanding these signals and their mechanisms in normal term pregnancies can provide insight into pathological activation of these signals, which can cause spontaneous preterm parturition. Hence, this review expands on our knowledge of exosomes as professional carriers of fetal signals to instigate human parturition.


2020 ◽  
Vol 12 (21) ◽  
pp. 3511
Author(s):  
Roghieh Eskandari ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh ◽  
Bahram Salehi ◽  
Brian Brisco ◽  
...  

Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the UAV capabilities of various applications. This systematic review was performed on studies through a statistical meta-analysis of UAV applications along with machine learning algorithms in agro-environmental monitoring. For this purpose, a total number of 163 peer-reviewed articles published in 13 high-impact remote sensing journals over the past 20 years were reviewed focusing on several features, including study area, application, sensor type, platform type, and spatial resolution. The meta-analysis revealed that 62% and 38% of the studies applied regression and classification models, respectively. Visible sensor technology was the most frequently used sensor with the highest overall accuracy among classification articles. Regarding regression models, linear regression and random forest were the most frequently applied models in UAV remote sensing imagery processing. Finally, the results of this study confirm that applying machine learning approaches on UAV imagery produces fast and reliable results. Agriculture, forestry, and grassland mapping were found as the top three UAV applications in this review, in 42%, 22%, and 8% of the studies, respectively.


2021 ◽  
Vol 12 ◽  
Author(s):  
Jia-Wei Tang ◽  
Qing-Hua Liu ◽  
Xiao-Cong Yin ◽  
Ya-Cheng Pan ◽  
Peng-Bo Wen ◽  
...  

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.


2021 ◽  
Author(s):  
Mohammadrahim Kazemzadeh ◽  
Colin Hisey ◽  
Kamran Zargar ◽  
Peter Xu ◽  
Neil Broderick

<div>Machine learning has shown great potential for classifying diverse samples in biomedical applications based on their Raman spectra. However, the acquired spectra typically require several preprocessing steps before standard machine learning algorithms can accurately and reliably classify them. To simplify this workflow and enable future growth of this technology, we present a unified solution for classifying biological Raman spectra without any need of prepossessing, including denoising and baseline establishment. This method is developed based on a custom version of a convolutional neural network (CNN) elicited from ResNet architecture, combined with our proposed data augmentation technique. The superiority of this method compared to conventional classification techniques is shown by applying it to Raman spectra of different grades of bladder cancer tissue and surface enhanced Raman spectroscopy (SERS) spectra of various strains of E. Coli extracellular vesicles (EVs). These results show that our method is far more robust compared to its conventional counterparts when dealing with the various kinds of spectral baselines produced by different Raman spectrometers.</div>


2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.


2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.


Sign in / Sign up

Export Citation Format

Share Document