Determination of phosphatidyl ethanol (PEth) 16:0/18:1 in dried blood samples of drivers involved in traffic accidents: A pilot study

Dağlioğlu, Nebīle; Özşeker, Pınar Efeoğlu; Dengiz, Huseyin; Kekeç, Zeynep

doi:10.1016/j.legalmed.2022.102091

Cited by 3 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study quantifying DBS PEth using an LC‐MS/MS method, the threshold of PEth for excessive alcohol consumption was determined to be 160 ng/mL when the blood alcohol concentration decision limit was 50 mg/dL. This threshold provided a sensitivity of 92% and a specificity of 90% for predicting excessive alcohol consumption 35 . In contrast, a threshold below 20 ng/mL is associated with determining alcohol abstinence.…”

Section: Applicationsmentioning

confidence: 99%

“…This threshold provided a sensitivity of 92% and a specificity of 90% for predicting excessive alcohol consumption. 35 In contrast, a threshold below 20 ng/mL is associated with determining alcohol abstinence. However, given the 4–7 days half‐life of PEth, it can take months for PEth values to fall below this threshold following cessation of alcohol consumption.…”

Section: Applicationsmentioning

confidence: 99%

“… 20 Furthermore, paper‐based ionization techniques such as paper spray ionization (PSI) for MS have enabled the direct analysis of a broad spectrum of analytes in dried microsamples without the requirement of any prior sample pretreatment. 21 These sophisticated approaches have driven the implementation of microsampling in various biomedical domains; newborn and metabolic screening, 22 , 23 biomarker research, 21 , 24 , 25 pharmacokinetic (PK) and pharmacodynamic (PD) studies, 26 , 27 therapeutic drug monitoring (TDM), 28 , 29 , 30 , 31 , 32 , 33 forensic toxicology, 34 , 35 , 36 sports anti‐doping, 37 , 38 , 39 metabolomics 22 , 40 , 41 and proteomics. 24 , 42 , 43 Since 70% of medical decisions are governed by diagnostic blood tests, 44 several medical technology companies are investing in the development of innovative at‐home blood microsampling devices to make blood work more accessible and convenient.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blood microsampling technologies: Innovations and applications in 2022

Thangavelu

Wouters

Kindt

et al. 2023

Analytical Science Advances

View full text Add to dashboard Cite

With the development of highly sensitive bioanalytical techniques, the volume of samples necessary for accurate analysis has reduced. Microsampling, the process of obtaining small amounts of blood, has thus gained popularity as it offers minimal‐invasiveness, reduced logistical costs and biohazard risks while simultaneously showing increased sample stability and a potential for the decentralization of the approach and at‐home self‐sampling. Although the benefits of microsampling have been recognised, its adoption in clinical practice has been slow. Several microsampling technologies and devices are currently available and employed in research studies for various biomedical applications. This review provides an overview of the state‐of‐the‐art in microsampling technology with a focus on the latest developments and advancements in the field of microsampling. Research published in the year 2022, including studies (i) developing strategies for the quantitation of analytes in microsamples and (ii) bridging and comparing the interchangeability between matrices and choice of technology for a given application, is reviewed to assess the advantages, challenges and limitations of the current state of microsampling. Successful implementation of microsampling in routine clinical care requires continued efforts for standardization and harmonization. Microsampling has been shown to facilitate data‐rich studies and a patient‐centric approach to healthcare and is foreseen to play a central role in the future digital revolution of healthcare through continuous monitoring to improve the quality of life.

show abstract

Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blood microsampling technologies: Innovations and applications in 2022

Thangavelu

Wouters

Kindt

et al. 2023

Analytical Science Advances

View full text Add to dashboard Cite

show abstract

“…TRAMER conducts research on data storage and the compilation of statistical results for traffic accidents. In one of their studies, TRAMER discovered that 983.808 road accidents occurred in the country overall in 2020 [5,6].…”

Section: Introduction (Gi̇ri̇ş)mentioning

confidence: 99%

Predict the Number of Traffic Accidents in Turkey by Using Machine Learning Techniques and Python Tools

AL-ASADI¹,

TAŞDEMİR²,

ÖRNEK³

2022

AIS

View full text Add to dashboard Cite

Traffic accidents have grown rapidly throughout the world in recent years, causing great loss of life and property. Therefore, predicting traffic accidents is very important for improving transportation and public safety. Machine learning (ML) is a subfield of artificial intelligence that can extract information from dataset and use statistical approaches to predict values. In this study, three ML techniques were applied to predict the number of dead or injured in traffic accidents in Turkey until 2029: linear regression, decision trees, and random forest. These techniques were tested using a real dataset obtained from the TUIK website. In the results, it was seen that linear regression (LR) had the best performance. This result shows the superiority of the approach in predicting road accidents. Ultimately, this study will help road transport and insurance agencies develop road safety strategies.

show abstract