scholarly journals Machine Learning Approach to Enhance the Performance of MNP-Labeled Lateral Flow Immunoassay

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenqiang Yan ◽  
Kan Wang ◽  
Hao Xu ◽  
Xuyang Huo ◽  
Qinghui Jin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Point Of Care ◽  
Diagnostic Methods ◽  
Quantitative Detection ◽  
Magnetic Signal ◽  
Serum Samples ◽  
Specificity And Sensitivity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Sandwich Assays

Abstract The use of magnetic nanoparticle (MNP)-labeled immunochromatography test strips (ICTSs) is very important for point-of-care testing (POCT). However, common diagnostic methods cannot accurately analyze the weak magnetic signal from ICTSs, limiting the applications of POCT. In this study, an ultrasensitive multiplex biosensor was designed to overcome the limitations of capturing and normalization of the weak magnetic signal from MNPs on ICTSs. A machine learning model for sandwich assays was constructed and used to classify weakly positive and negative samples, which significantly enhanced the specificity and sensitivity. The potential clinical application was evaluated by detecting 50 human chorionic gonadotropin (HCG) samples and 59 myocardial infarction serum samples. The quantitative range for HCG was 1–1000 mIU mL−1 and the ideal detection limit was 0.014 mIU mL−1, which was well below the clinical threshold. Quantitative detection results of multiplex cardiac markers showed good linear correlations with standard values. The proposed multiplex assay can be readily adapted for identifying other biomolecules and also be used in other applications such as environmental monitoring, food analysis, and national security.

scholarly journals COVID-19 Biomarkers and Advanced Sensing Technologies for Point-of-Care (POC) Diagnosis

2021 ◽  
Vol 8 (7) ◽  
pp. 98
Author(s):  
Ernst Emmanuel Etienne ◽  
Bharath Babu Nunna ◽  
Niladri Talukder ◽  
Yudong Wang ◽  
Eon Soo Lee
Keyword(s):  
Response Times ◽  
Rna Virus ◽  
Point Of Care ◽  
Protein Detection ◽  
Turnaround Time ◽  
Quantitative Detection ◽  
Rt Pcr ◽  
Specificity And Sensitivity ◽  
Small Market ◽  
Multiple Biomarkers

COVID-19, also known as SARS-CoV-2 is a novel, respiratory virus currently plaguing humanity. Genetically, at its core, it is a single-strand positive-sense RNA virus. It is a beta-type Coronavirus and is distinct in its structure and binding mechanism compared to other types of coronaviruses. Testing for the virus remains a challenge due to the small market available for at-home detection. Currently, there are three main types of tests for biomarker detection: viral, antigen and antibody. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) remains the gold standard for viral testing. However, the lack of quantitative detection and turnaround time for results are drawbacks. This manuscript focuses on recent advances in COVID-19 detection that have lower limits of detection and faster response times than RT-PCR testing. The advancements in sensing platforms have amplified the detection levels and provided real-time results for SARS-CoV-2 spike protein detection with limits as low as 1 fg/mL in the Graphene Field Effect Transistor (FET) sensor. Additionally, using multiple biomarkers, detection levels can achieve a specificity and sensitivity level comparable to that of PCR testing. Proper biomarker selection coupled with nano sensing detection platforms are key in the widespread use of Point of Care (POC) diagnosis in COVID-19 detection.

scholarly journals Aptamer based diagnosis of crimean-congo hemorrhagic fever from clinical specimens

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tahmineh Jalali ◽  
Mostafa Salehi-Vaziri ◽  
Mohammad Hassan Pouriayevali ◽  
Seyed Latif Mousavi Gargari
Keyword(s):  
Point Of Care ◽  
Hemorrhagic Fever ◽  
Diagnostic Methods ◽  
Clinical Samples ◽  
Crimean Congo Hemorrhagic Fever ◽  
Point Of Care Test ◽  
Specificity And Sensitivity ◽  
Rural And Remote Areas ◽  
Cchf Virus ◽  
Exponential Enrichment

AbstractCrimean-Congo hemorrhagic fever (CCHF) is an acute viral zoonotic disease. The widespread geographic distribution of the disease and the increase in the incidence of the disease from new regions, placed CCHF in a list of public health emergency contexts. The rapid diagnosis, in rural and remote areas where the majority of cases occur, is essential for patient management. Aptamers are considered as a specific and sensitive tool for being used in rapid diagnostic methods. The Nucleoprotein (NP) of the CCHF virus (CCHFV) was selected as the target for the isolation of aptamers based on its abundance and conservative structure, among other viral proteins. A total of 120 aptamers were obtained through 9 rounds of SELEX (Systematic Evolution of Ligands by Exponential Enrichment) from the ssDNA aptamer library, including the random 40-nucleotide ssDNA region between primer binding sites (GCCTGTTGTGAGCCTCCTAAC(N40)GGGAGACAAGAATAAGCA). The KD of aptamers was calculated using the SPR technique. The Apt33 with the highest affinity to NP was selected to design the aptamer-antibody ELASA test. It successfully detected CCHF NP in the concentration of 90 ng/ml in human serum. Evaluation of aptamer-antibody ELASA with clinical samples showed 100% specificity and sensitivity of the test. This simple, specific, and the sensitive assay can be used as a rapid and early diagnosis tool, as well as the use of this aptamer in point of care test near the patient. Our results suggest that the discovered aptamer can be used in various aptamer-based rapid diagnostic tests for the diagnosis of CCHF virus infection.

scholarly journals A machine learning approach to predicting short-term mortality risk in patients starting chemotherapy

2017 ◽  
Author(s):  
Aymen A. Elfiky ◽  
Maximilian J. Pany ◽  
Ravi B. Parikh ◽  
Ziad Obermeyer
Keyword(s):  
Machine Learning ◽  
Mortality Risk ◽  
Palliative Chemotherapy ◽  
Learning Algorithm ◽  
Cancer Center ◽  
Short Term ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Short Term Mortality ◽  
And Performance

ABSTRACTBackgroundCancer patients who die soon after starting chemotherapy incur costs of treatment without benefits. Accurately predicting mortality risk from chemotherapy is important, but few patient data-driven tools exist. We sought to create and validate a machine learning model predicting mortality for patients starting new chemotherapy.MethodsWe obtained electronic health records for patients treated at a large cancer center (26,946 patients; 51,774 new regimens) over 2004-14, linked to Social Security data for date of death. The model was derived using 2004-11 data, and performance measured on non-overlapping 2012-14 data.Findings30-day mortality from chemotherapy start was 2.1%. Common cancers included breast (21.1%), colorectal (19.3%), and lung (18.0%). Model predictions were accurate for all patients (AUC 0.94). Predictions for patients starting palliative chemotherapy (46.6% of regimens), for whom prognosis is particularly important, remained highly accurate (AUC 0.92). To illustrate model discrimination, we ranked patients initiating palliative chemotherapy by model-predicted mortality risk, and calculated observed mortality by risk decile. 30-day mortality in the highest-risk decile was 22.6%; in the lowest-risk decile, no patients died. Predictions remained accurate across all primary cancers, stages, and chemotherapies—even for clinical trial regimens that first appeared in years after the model was trained (AUC 0.94). The model also performed well for prediction of 180-day mortality (AUC 0.87; mortality 74.8% in the highest risk decile vs. 0.2% in the lowest). Predictions were more accurate than data from randomized trials of individual chemotherapies, or SEER estimates.InterpretationA machine learning algorithm accurately predicted short-term mortality in patients starting chemotherapy using EHR data. Further research is necessary to determine generalizability and the feasibility of applying this algorithm in clinical settings.

scholarly journals Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes

DYNA ◽  
2020 ◽  
Vol 87 (212) ◽  
pp. 63-72
Author(s):  
Jorge Iván Pérez Rave ◽  
Favián González Echavarría ◽  
Juan Carlos Correa Morales
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Approach ◽  
Predictive Capability ◽  
Predictive Capacity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Property Price ◽  
Object Of Study ◽  
Online Pricing

The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assembly (random forest and bagging) show predictive superiority with respect to others. The attributes derived from the text had a significant relationship with the property price (on a log scale). However, their contribution to the predictive capacity was almost nil, since four different attributes achieved highly accurate predictions and remained stable when the sample change.

scholarly journals Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen

2019 ◽  
Vol 14 (3) ◽  
pp. 302-307
Author(s):  
Benjamin Q. Huynh ◽  
Sanjay Basu
Keyword(s):  
Machine Learning ◽  
Food Prices ◽  
Internally Displaced ◽  
Fuel Prices ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Using Data ◽  
Diverse Data ◽  
Internally Displaced Population ◽  
Persistence Model

ABSTRACTObjectives:Armed conflict has contributed to an unprecedented number of internally displaced persons (IDPs), individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when IDPs will migrate to an area remains a major challenge for aid delivery organizations. We sought to develop an IDP migration forecasting framework that could empower humanitarian aid groups to more effectively allocate resources during conflicts.Methods:We modeled monthly IDP migration between provinces within Syria and within Yemen using data on food prices, fuel prices, wages, location, time, and conflict reports. We compared machine learning methods with baseline persistence methods of forecasting.Results:We found a machine learning approach that more accurately forecast migration trends than baseline persistence methods. A random forest model outperformed the best persistence model in terms of root mean square error of log migration by 26% and 17% for the Syria and Yemen datasets, respectively.Conclusions:Integrating diverse data sources into a machine learning model appears to improve IDP migration prediction. Further work should examine whether implementation of such models can enable proactive aid allocation for IDPs in anticipation of forecast arrivals.

scholarly journals Automatic subtyping of individuals with Primary Progressive Aphasia

2020 ◽  
Author(s):  
Charalambos Themistocleous ◽  
Bronte Ficek ◽  
Kimberly Webster ◽  
Dirk-Bart den Ouden ◽  
Argye E. Hillis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Primary Progressive Aphasia ◽  
Support Vector ◽  
Progressive Aphasia ◽  
Primary Progressive ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Automated Machine Learning

AbstractBackgroundThe classification of patients with Primary Progressive Aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists.ObjectiveThe aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA.MethodsIn this paper, we present a machine learning model based on Deep Neural Networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as expert clinicians’ classifications.ResultsThe DNN model outperformed the other machine learning models with 80% classification accuracy, providing reliable subtyping of patients with PPA into variants and it even outperformed auditory classification of patients into variants by clinicians.ConclusionsWe show that the combined speech and language markers from connected speech productions provide information about symptoms and variant subtyping in PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick and inexpensive classification of patients with PPA.

scholarly journals Quantitative Toxicity Prediction via Ensembling of Heterogeneous Predictors

2019 ◽  
Author(s):  
Abdul Karim ◽  
Vahid Riahi ◽  
Avinash Mishra ◽  
Abdollah Dehzangi ◽  
M. A. Hakim Newton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Models ◽  
Individual Performance ◽  
Learning Model ◽  
Data Representation ◽  
Toxicity Prediction ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Benchmark Datasets

Abstract Representing molecules in the form of only one type of features and using those features to predict their activities is one of the most important approaches for machine-learning-based chemical-activity-prediction. For molecular activities like quantitative toxicity prediction, the performance depends on the type of features extracted and the machine learning approach used. For such cases, using one type of features and machine learning model restricts the prediction performance to specific representation and model used. In this paper, we study quantitative toxicity prediction and propose a machine learning model for the same. Our model uses an ensemble of heterogeneous predictors instead of typically using homogeneous predictors. The predictors that we use vary either on the type of features used or on the deep learning architecture employed. Each of these predictors presumably has its own strengths and weaknesses in terms of toxicity prediction. Our motivation is to make a combined model that utilizes different types of features and architectures to obtain better collective performance that could go beyond the performance of each individual predictor. We use six predictors in our model and test the model on four standard quantitative toxicity benchmark datasets. Experimental results show that our model outperforms the state-of-the-art toxicity prediction models in 8 out of 12 accuracy measures. Our experiments show that ensembling heterogeneous predictor improves the performance over single predictors and homogeneous ensembling of single predictors.The results show that each data representation or deep learning based predictor has its own strengths and weaknesses, thus employing a model ensembling multiple heterogeneous predictors could go beyond individual performance of each data representation or each predictor type.

scholarly journals Machine Learning Model for GSM BSC Control Plane Units

2019 ◽  
Vol 8 (6S) ◽  
pp. 219-223
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Model Parameters ◽  
Large Set ◽  
Data Set ◽  
Wide Acceptance ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Accuracy Of Prediction

At maximum traffic intensity i.e. during the busy hour, the GSM BSC signalling units (BSU) measured CPU load will be at its peak. The BSUs CPU load is a function of the number of transceivers (TRXs) mapped to it and hence the volume of offered traffic being handled by the unit. The unit CPU load is also a function of the nature of the offered load, i.e. with the same volume of offered load, the CPU load with the nominal traffic profile would be different as compared to some other arbitrary traffic profile. To manage future traffic growth, a model to estimate the BSU unit CPU load is an essential need. In recent times, using Machine Learning (ML) to develop such a model is an approach that has gained wide acceptance. Since, the estimation of CPU load is difficult as it depends on large set of parameters, machine learning approach is more scalable. In this paper, we describe a back-propagation neural network model that was developed to estimate the BSU unit CPU load. We describe the model parameters and choices and implementation architecture, and estimate its accuracy of prediction, based on an evaluation data set. We also discuss alternative ML architectures and compare their relative prediction accuracies, to the primary ML model

scholarly journals Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Daowei Li ◽  
Qiang Zhang ◽  
Yue Tan ◽  
Xinghuo Feng ◽  
Yuanyi Yue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Laboratory Tests ◽  
Operating Characteristic ◽  
Clinical Laboratory ◽  
Severe Disease ◽  
Ct Images ◽  
Model Combining ◽  
Machine Learning Model ◽  
Machine Learning Approach

BACKGROUND Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. OBJECTIVE This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. METHODS A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients’ CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. RESULTS We present a prediction model combining patients’ radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F<sub>1</sub> score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients’ laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. CONCLUSIONS To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

scholarly journals Evaluation of SARS-CoV-2 Antibody Point of Care Devices in the Laboratory and Clinical Setting

2021 ◽  
Author(s):  
Kirsty McCance ◽  
Helen Wise ◽  
Jennifer Simpson ◽  
Becky Bachelor ◽  
Harriet Hale ◽  
...  
Keyword(s):  
Immune Status ◽  
Point Of Care ◽  
Capillary Blood ◽  
Serum Samples ◽  
Specificity And Sensitivity ◽  
Independent Evaluation ◽  
Positive Antibody ◽  
Antibody Titres ◽  
Paired Serum ◽  
Antibody Tests

SARS-CoV-2 Antibody tests have been marketed to diagnose previous SARS-CoV-2 infection and as a test of immune status. There is a lack of evidence on the performance and clinical utility of these tests. We aimed to carry out an evaluation of 14 point of care (POC) SARS-CoV-2 antibody tests. Serum from participants with previous RT-PCR (Real-Time Polymerase chain reaction) confirmed SARS-CoV-2 infection and pre-pandemic controls were used to determine specificity and sensitivity of each POC device. Changes in sensitivity with increasing time from infection were determined on a cohort of participants. Corresponding neutralising antibody status was measured to establish whether the detection of antibodies by the POC device correlated with immune status. Paired capillary and serum samples were collected to ascertain whether POC devices performed comparably on capillary samples. Sensitivity and specificity varied between the POC devices and in general did not meet the manufacturers reported performance characteristics signifying the importance of independent evaluation of these tests. The sensitivity peaked at >20 days following symptoms onset however sensitivity of 3 POC devices evaluated at extended time points showed that sensitivity declined with time and this was particularly marked at >140 days post infection onset. This is relevant if the tests are to be used for sero-prevelence studies. Neutralising antibody data showed positive antibody results on POC devices did not necessarily confer high neutralising antibody titres and these POC devices cannot be used to determine immune status to the SARS-CoV-2 virus. Comparison of paired serum and capillary results showed that there was a decline in sensitivity using capillary blood. This has implications in the utility of the test as they are designed to be used on capillary blood by the general population.

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