scholarly journals Multiplexed measurement of protein biomarkers in high-frequency longitudinal dried blood spot (DBS) samples: characterization of inflammatory responses

2019 ◽  
Author(s):  
N. Leigh Anderson ◽  
Morteza Razavi ◽  
Matthew E. Pope ◽  
Richard Yip ◽  
Terry W. Pearson
Keyword(s):  
Temporal Dynamics ◽  
Inflammatory Responses ◽  
Temporal Analysis ◽  
Dried Blood Spots ◽  
Blood Protein ◽  
Spectrometric Method ◽  
Mass Spectrometric Method ◽  
Learning Approaches ◽  
Protein Biomarkers ◽  
Over Time

AbstractA detailed understanding of changes in blood protein biomarkers occuring in individuals over time would enable truly personalized approaches to health and disease monitoring. Such measurements could reveal smaller, earlier departures from normal baseline levels of biomarkers thus allowing better disease detection and treatment monitoring. Current practice, however, generally involves infrequent, sporadic biomarker testing, and this undersampling likely fails to capture important biological phenomena. Here we report the use of a robust multiplex immuno-mass spectrometric method (SISCAPA) to measure a panel of clinically-relevant proteins in a unique collection of 1,522 dried blood spots collected longitudinally by 8 individuals over periods of up to 9 years, with daily sampling during some intervals. Analytical workflow CVs of 2-6% for most assays were achieved by normalizing DBS plasma volume using a set of 3 minimally varying proteins, facilitating temporal analysis of both high- and low-amplitude biomarker changes compared to personalized baselines. The biomarkers included a panel of 9 positive and 5 negative acute phase response (inflammatory) proteins, allowing longitudinal analysis of inflammation markers associated with major and minor infections, influenza vaccination, recovery from hip-replacement surgery and Crohn’s disease. The results illustrate complex time-dependent “biomarker trajectories” on multiple timescales and provide a basis for detailed personalized models of inflammation dynamics. The striking stability of most biomarker protein levels over time, combined with the convenience of self-sampling and low cost of multiplexed measurements using mass spectrometry, provide a new window into the temporal dynamics of disease processes. The extensive results obtained using this high throughput approach offer a new source of precision biomarker ‘big data’ amenable to machine learning approaches and application to more personalized health monitoring.

scholarly journals Concentration Determination of >200 Proteins in Dried Blood Spots for Biomarker Discovery and Validation

2020 ◽  
Vol 19 (3) ◽  
pp. 540-553 ◽  
Author(s):  
Azad Eshghi ◽  
Adam J. Pistawka ◽  
Jun Liu ◽  
Michael Chen ◽  
Nicholas J. T. Sinclair ◽  
...  
Keyword(s):  
Protein Concentration ◽  
Biomarker Discovery ◽  
De Novo ◽  
Multiple Reaction Monitoring ◽  
Dried Blood Spots ◽  
Cold Chain ◽  
Spectrometric Method ◽  
Mass Spectrometric Method ◽  
Protein Biomarkers ◽  
Limit Of Quantification

The use of protein biomarkers as surrogates for clinical endpoints requires extensive multilevel validation including development of robust and sensitive assays for precise measurement of protein concentration. Multiple reaction monitoring (MRM) is a well-established mass-spectrometric method that can be used for reproducible protein-concentration measurements in biological specimens collected via microsampling. The dried blood spot (DBS) microsampling technique can be performed non-invasively without the expertise of a phlebotomist, and can enhance analyte stability which facilitate the application of this technique in retrospective studies while providing lower storage and shipping costs, because cold-chain logistics can be eliminated. Thus, precise, sensitive, and multiplexed methods for measuring protein concentrations in DBSs can be used for de novo biomarker discovery and for biomarker quantification or verification experiments. To achieve this goal, MRM assays were developed for multiplexed concentration measurement of proteins in DBSs.The lower limit of quantification (LLOQ) was found to have a median total coefficient of variation (CV) of 18% for 245 proteins, whereas the median LLOQ was 5 fmol of peptide injected on column, and the median inter-day CV over 4 days for measuring endogenous protein concentration was 8%. The majority (88%) of the assays displayed parallelism, whereas the peptide standards remained stable throughout the assay workflow and after exposure to multiple freeze-thaw cycles. For 190 proteins, the measured protein concentrations remained stable in DBS stored at ambient laboratory temperature for up to 2 months. Finally, the developed assays were used to measure the concentration ranges for 200 proteins in twenty same sex, same race and age matched individuals.

scholarly journals Discovery and Temporal Analysis of MOOC Study Patterns

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mina Shirvani Boroujeni ◽  
Pierre Dillenbourg
Keyword(s):  
Online Learning ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Activity Patterns ◽  
Synthetic Data ◽  
Temporal Analysis ◽  
Time Step ◽  
Data Driven Approach ◽  
Learning Behaviours ◽  
Over Time

The large-scale and granular interaction data collected in online learning platforms such as massive open online courses (MOOCs) provide unique opportunities to better understand individuals’ learning processes and could facilitate the design of personalized and more effective support mechanisms for learners. In this paper, we present two different methods of extracting study patterns from activity sequences. Unlike most of the previous works, with post hoc analysis of activity patterns, our proposed methods could be deployed during the course and enable the learners to receive real-time support and feedback. In the first method, following a hypothesis-driven approach, we extract predefined patterns from learners’ interactions with the course materials. We then identify and analyze different longitudinal profiles among learners by clustering their study pattern sequences during the course. Our second method is a data-driven approach to discover latent study patterns and track them over time in a completely unsupervised manner. We propose a clustering pipeline to model and cluster activity sequences at each time step and then search for matching clusters in previous steps to enable tracking over time. The proposed pipeline is general and allows for analysis at different levels of action granularity and time resolution in various online learning environments. Experiments with synthetic data show that our proposed method can accurately detect latent study patterns and track changes in learning behaviours. We demonstrate the application of both methods on a MOOC dataset and study the temporal dynamics of learners’ behaviour in this context.

Rapid diagnosis of phenylketonuria by quantitative analysis for phenylalanine and tyrosine in neonatal blood spots by tandem mass spectrometry

1993 ◽  
Vol 39 (1) ◽  
pp. 66-71 ◽  
Author(s):  
D H Chace ◽  
D S Millington ◽  
N Terada ◽  
S G Kahler ◽  
C R Roe ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Neonatal Screening ◽  
Screening Method ◽  
Dried Blood Spots ◽  
Spectrometric Method ◽  
Tandem Mass ◽  
Mass Spectrometric Method ◽  
Blood Spots ◽  
Minimal Sample

Abstract A new method for quantifying specific amino acids in small volumes of plasma and whole blood has been developed. Based on isotope-dilution tandem mass spectrometry, the method takes only a few minutes to perform and requires minimal sample preparation. The accurate assay of both phenylalanine and tyrosine in dried blood spots used for neonatal screening for phenylketonuria in North Carolina successfully differentiated infants who had been classified as normal, affected, and falsely positive by current fluorometric methods. Because the mass-spectrometric method also recognizes other aminoacidemias simultaneously and is capable of automation, it represents a useful development toward a broad-spectrum neonatal screening method.

scholarly journals Elevated Homocysteine after Elevated Propionylcarnitine or Low Methionine in Newborn Screening Is Highly Predictive for Low Vitamin B12 and Holo-Transcobalamin Levels in Newborns

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 626
Author(s):  
Tomaž Rozmarič ◽  
Goran Mitulović ◽  
Vassiliki Konstantopoulou ◽  
Bernadette Goeschl ◽  
Martina Huemer ◽  
...  
Keyword(s):  
Newborn Screening ◽  
Vitamin B12 ◽  
Methylmalonic Acid ◽  
Incidental Finding ◽  
Dried Blood Spots ◽  
Neurological Deficits ◽  
Spectrometric Method ◽  
Mass Spectrometric Method ◽  
Blood Spots ◽  
Total Homocysteine

Early diagnostics and treatment of vitamin B12 deficiency (B12D) in infants, mainly maternally conditioned, is crucial in preventing possible developmental delay and neurological deficits. Currently, B12D is rarely listed in regular newborn screening panels and mostly regarded as an incidental finding. The aim of this study was to evaluate a targeted newborn screening strategy for detection of suspected B12D. A decision strategy based on the primary parameters propionylcarnitine and methionine for selection of samples to be analyzed for total homocysteine by mass spectrometry was established. Therefore, 93,116 newborns were initially screened. Concentrations of vitamin B12 and holotranscobalamin in serum were obtained from clinical follow-up analyses of recalled newborns. Moreover, an extremely sensitive mass spectrometric method to quantify methylmalonic acid from the dried blood spots was developed. Overall, 0.15% of newborns were screened positive for suspected B12D, of which 64% had vitamin B12 concentrations below 148 pM. We also determined a cutoff value for methylmalonic acid in dried blood spots indicative for B12D in infants. Overall, we calculated a prevalence of 92/100,000 for suspected B12D in the Austrian newborns. In conclusion, we present a screening algorithm including second-tier measurement of total homocysteine that allows detection of low B12 serum concentrations with a high detection rate and low false-positive rate.

scholarly journals Word embeddings quantify 100 years of gender and ethnic stereotypes

2018 ◽  
Vol 115 (16) ◽  
pp. E3635-E3644 ◽  
Author(s):  
Nikhil Garg ◽  
Londa Schiebinger ◽  
Dan Jurafsky ◽  
James Zou
Keyword(s):  
United States ◽  
Machine Learning ◽  
Temporal Dynamics ◽  
Temporal Analysis ◽  
The United States ◽  
English Word ◽  
Attitudes Toward Women ◽  
Word Embeddings ◽  
Asian Immigration ◽  
Over Time

Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts—e.g., the women’s movement in the 1960s and Asian immigration into the United States—and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.

scholarly journals Intelligent Drought Tracking for its Use in Machine Learning: Implementation and First Results

10.29007/klgg ◽  
2018 ◽  
Author(s):  
Vitali Diaz ◽  
Gerald A. Corzo Perez ◽  
Henny A.J. Van Lanen ◽  
Dimitri Solomatine
Keyword(s):  
Machine Learning ◽  
Temporal Dynamics ◽  
Temporal Analysis ◽  
Drought Monitoring ◽  
Grid Data ◽  
Drought Prediction ◽  
Documented Information ◽  
First Results ◽  
Spatio Temporal ◽  
Over Time

Due to the underlying characteristics of drought, monitoring of its spatio-temporal development is difficult. Last decades, drought monitoring have been increasingly developed, however, including its spatio-temporal dynamics is still a challenge. This study proposes a method to monitor drought by tracking its spatial extent. A methodology to build drought trajectories is introduced, which is put in the framework of machine learning (ML) for drought prediction. Steps for trajectories calculation are (1) spatial areas computation, (2) centroids localization, and (3) centroids linkage. The spatio- temporal analysis performed here follows the Contiguous Drought Area (CDA) analysis. The methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. Results show regions where drought with considerable coverage tend to occur, and suggest possible concurrent routes. Tracks of six of the most severe reported droughts were analysed. In all of them, areas overlap considerably over time, which suggest that drought remains in the same region for a period of time. Years with the largest drought areas were 2000 and 2002, which coincide with documented information presented. Further research is under development to setup the ML model to predict the track of drought.

Tandem Mass Spectrometric Evaluation of Core Structures of Aromatic Compounds after Catalytic Deoxygenation

2017 ◽  
Author(s):  
Xueming Dong
Keyword(s):  
Aromatic Compounds ◽  
Supported Catalyst ◽  
Mass Spectrometric ◽  
Spectrometric Method ◽  
Mass Spectrometric Method ◽  
Aromatic Rings ◽  
Tandem Mass Spectrometric ◽  
Catalytic Deoxygenation ◽  
Oxygen Atoms

Catalytic deoxygenation of coal enhances the stability and combustion performance of coal-derived liquids. However, determination of the selectivity of removal of oxygen atoms incorporated in or residing outside of aromatic rings is challenging. This limits the ability to evaluate the success of catalytic deoxygenation processes. A mass spectrometric method, in-source collision-activated dissociation (ISCAD), combined with high resolution product ion detection, is demonstrated to allow the determination of whether the oxygen atoms in aromatic compounds reside outside of aromatic rings or are part of the aromatic system, because alkyl chains can be removed from aromatic cores via ISCAD. Application of this method for the analysis of a subbituminous coal treated using a supported catalyst revealed that the catalytic treatment reduced the number of oxygen-containing heteroaromatic rings but not the number of oxygen atoms residing outside the aromatic rings.<br>

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