In this study, we describe the transfer of a new and fully automated workflow for the cost-effective drug screening of large populations based on the dried blood spot (DBS) technology. The method was installed at a routine poison control center and applied for DBS and dried urine spot (DUS) samples. A fast method focusing on the high-interest drugs and an extended screening method were developed on the automated platform. The dried cards were integrated into the automated workflow, in which the cards were checked in a camera recognition system, spiked with deuterated standards via an in-built spraying module and directly extracted. The extract was transferred online to an analytical LC column and then to the electrospray ionization tandem mass spectrometry system. The target compounds were analyzed in positive multiple-reaction monitoring mode. Before each sample batch or analysis day, calibration samples were measured to balance inter-day variations and to avoid false negative samples. An internal standard was integrated prior the sample extraction to allow in process control. A total of 28 target compounds were analyzed and directly extracted within 5 min per sample. This fast screening method was then extended to 20 min, enabling the usage of a Forensic Toxicology Database to screen over 1,200 drugs. The method gives confident positive/negative results for all tested drugs at their individual cut-off concentration. Good precision (±15%, respectively ±20% at limit of quantification) and correlation within the calibration range from 5 to 1,000 ng/mL was obtained. The method was finally applied to real cases from the lab and cross-checked with the existing methodologies.
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Front Neurorobot
December 2024
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
Existing image fusion methods primarily focus on complex network structure designs while neglecting the limitations of simple fusion strategies in complex scenarios. To address this issue, this study proposes a new method for infrared and visible image fusion based on a multimodal large language model. The method proposed in this paper fully considers the high demand for semantic information in enhancing image quality as well as the fusion strategies in complex scenes.
View Article and Find Full Text PDFSci Rep
January 2025
College of Civil and Transportation Engineering, Hohai University, No. 1 Xikang Road, Nanjing City, 210098, Jiangsu Province, People's Republic of China.
Aftershocks can cause additional damage or even lead to the collapse of structures already weakened by a mainshock. Scarcity of in-situ recorded aftershock accelerograms heightens the need to develop synthetic aftershock ground motions. These synthesized motions are crucial for assessing the cumulative seismic demand on structures subjected to mainshock-aftershock sequences.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Radiology, Stanford University, 1201 Welch Rd, P270, Stanford, California, 94305-6104, UNITED STATES.
Radiation dose and diagnostic image quality are opposing constraints in x-ray CT. Conventional methods do not fully account for organ-level radiation dose and noise when considering radiation risk and clinical task. In this work, we develop a pipeline to generate individualized organ-specific dose and noise at desired dose levels from clinical CT scans.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China. Electronic address:
Background And Objective: Neurosurgical navigation is a critical element of brain surgery, and accurate segmentation of brain and scalp blood vessels is crucial for surgical planning and treatment. However, conventional methods for segmenting blood vessels based on statistical or thresholding techniques have limitations. In recent years, deep learning-based methods have emerged as a promising solution for blood vessel segmentation, but the segmentation of small blood vessels and scalp blood vessels remains challenging.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
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