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Quick, easy, cheap, effective, rugged, and safe extraction strategies are becoming increasingly adopted in various analytical fields to determine drugs in biological specimens. In the present study, we developed two fully automated quick, easy, cheap, effective, rugged, and safe extraction methods based on acetonitrile salting-out assisted liquid-liquid extraction (method 1) and acetonitrile salting-out assisted liquid-liquid extraction followed by dispersive solid-phase extraction (method 2) using a commercially available automated liquid-liquid extraction system. We applied these methods to the extraction of 14 psychotropic drugs (11 benzodiazepines and carbamazepine, quetiapine, and zolpidem) from whole blood samples. Both methods prior to liquid chromatography-tandem mass spectrometry analysis exhibited high linearity of calibration curves (correlation coefficients, > 0.9997), ppt level detection sensitivities, and satisfactory precisions (< 8.6% relative standard deviation), accuracies (within ± 16% relative error), and matrix effects (81-111%). Method 1 provided higher recovery rates (80-91%) than method 2 (72-86%), whereas method 2 provided higher detection sensitivities (limits of detection, 0.003-0.094 ng/mL) than method 1 (0.025-0.47 ng/mL) owing to the effectiveness of its dispersive solid-phase extraction cleanup step. These fully automated extraction methods realize reliable, labor-saving, user-friendly, and hygienic extraction of target analytes from whole blood samples.
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http://dx.doi.org/10.1002/jssc.202200681 | DOI Listing |
J Med Internet Res
September 2025
School of Advertising, Marketing and Public Relations, Faculty of Business and Law, Queensland University of Technology, Brisbane, Australia.
Background: Labor shortages in health care pose significant challenges to sustaining high-quality care for people with intellectual disabilities. Social robots show promise in supporting both people with intellectual disabilities and their health care professionals; yet, few are fully developed and embedded in productive care environments. Implementation of such technologies is inherently complex, requiring careful examination of facilitators and barriers influencing sustained use.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Epilepsy, a highly individualized neurological disorder, affects millions globally. Electroencephalography (EEG) remains the cornerstone for seizure diagnosis, yet manual interpretation is labor-intensive and often unreliable due to the complexity of multi-channel, high-dimensional data. Traditional machine learning models often struggle with overfitting and fail in fully capturing the highdimensional, temporal dynamics of EEG signals, restricting their clinical utility.
View Article and Find Full Text PDFJ Magn Reson Imaging
September 2025
Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA.
The morphological patterns of lung adenocarcinoma (LUAD) are recognized for their prognostic significance, with ongoing debate regarding the optimal grading strategy. This study aimed to develop a clinical-grade, fully quantitative, and automated tool for pattern classification/quantification (PATQUANT), to evaluate existing grading strategies, and determine the optimal grading system. PATQUANT was trained on a high-quality dataset, manually annotated by expert pathologists.
View Article and Find Full Text PDFJ Appl Clin Med Phys
September 2025
Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Clinical Research Center for Radiation Oncology, Shanghai Key Laboratory of Radiation Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Purpose: This study aims to assess percentage of automated AIO plans that met clinical treatment standards of radiotherapy plans generated by the fully automated All-in-one (AIO) process.
Methods: The study involved 117 rectal cancer patients who underwent AIO treatment. Fully automated regions of interest (ROI) and treatment plans were developed without manual intervention, comparing them to manually generated plans used in clinical practice.