The goal of this work was to provide a technical solution for the automated optimization of multi-column systems for protein separation and fractionation. Both algorithm and a software that can be downloaded are provided. In this algorithm, the length and order of the individual column segments can be considered. Various solutions are provided by the algorithm, including i) to obtain uniform peak distribution, ii) to park the different species at the inlet of the individual column segments, and iii) to elute all species as a single peak. Two representative examples are presented, showing the possibility to obtain uniform selectivity between monoclonal antibody (mAb) sub-units, and the on-column fractioning of intact mAbs.
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http://dx.doi.org/10.1016/j.chroma.2020.461838 | DOI Listing |
J Med Internet Res
January 2025
Department of Cardiology, Yonsei University College of Medicine, Seoul, Republic of Korea.
Background: Efficient emergency patient transport systems, which are crucial for delivering timely medical care to individuals in critical situations, face certain challenges. To address this, CONNECT-AI (CONnected Network for EMS Comprehensive Technical-Support using Artificial Intelligence), a novel digital platform, was introduced. This artificial intelligence (AI)-based network provides comprehensive technical support for the real-time sharing of medical information at the prehospital stage.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Psychology, City College, City University of New York, New York, NY 10031.
Looking at the world often involves not just seeing things, but feeling things. Modern feedforward machine vision systems that learn to perceive the world in the absence of active physiology, deliberative thought, or any form of feedback that resembles human affective experience offer tools to demystify the relationship between seeing and feeling, and to assess how much of visually evoked affective experiences may be a straightforward function of representation learning over natural image statistics. In this work, we deploy a diverse sample of 180 state-of-the-art deep neural network models trained only on canonical computer vision tasks to predict human ratings of arousal, valence, and beauty for images from multiple categories (objects, faces, landscapes, art) across two datasets.
View Article and Find Full Text PDFCurr Pain Headache Rep
January 2025
Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.
Purpose Of Review: Artificial intelligence (AI) offers a new frontier for aiding in the management of both acute and chronic pain, which may potentially transform opioid prescribing practices and addiction prevention strategies. In this review paper, not only do we discuss some of the current literature around predicting various opioid-related outcomes, but we also briefly point out the next steps to improve trustworthiness of these AI models prior to real-time use in clinical workflow.
Recent Findings: Machine learning-based predictive models for identifying risk for persistent postoperative opioid use have been reported for spine surgery, knee arthroplasty, hip arthroplasty, arthroscopic joint surgery, outpatient surgery, and mixed surgical populations.
Med Biol Eng Comput
January 2025
Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection of metastatic tumors and response to therapy assessment due to its high sensitivity compared to anatomical imaging modalities. The balance between image quality and radiation exposure is critical, as reducing the administered dose results in a lower signal-to-noise ratio (SNR) and information loss, which may significantly affect clinical diagnosis. Deep learning (DL) algorithms have recently made significant progress in low-dose (LD) PET reconstruction.
View Article and Find Full Text PDFIntensive Care Med
January 2025
Global Health Research Group in Acquired Brain and Spine Injuries, Cambridge, UK.
Background: Invasive systems are commonly used for monitoring intracranial pressure (ICP) in traumatic brain injury (TBI) and are considered the gold standard. The availability of invasive ICP monitoring is heterogeneous, and in low- and middle-income settings, these systems are not routinely employed due to high cost or limited accessibility. The aim of this consensus was to develop recommendations to guide monitoring and ICP-driven therapies in TBI using non-invasive ICP (nICP) systems.
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