Chemical detection of tuberculosis (TB) products in sputum was attempted by using gas chromatographic analysis in conjunction with different pattern recognition computer models. For the chromatographic separations, we used a 2 mm x 1.8 m packed column and a 0.25 mm x 30 m fused silica capillary column to analyse the methylated glycosides and fatty acid methyl ester derivatives. Three computer pattern recognition methods were applied: error score, TB score and discriminant analysis. These methods predicted the presence of active TB most often in sputa of active TB patients and less so in those from inactive, suspected and non-TB patients, in that order. Although the best true positive of 75% was obtained from the TB score method and best true negative of 98% from discriminant analysis, the accompanying false positive and false negative results (36% and 50%, respectively) were unacceptable. The use of capillary column or fatty acid methyl ester derivatives of the samples did not improve on the predictive values of chromatograms obtained from the packed column on trimethylsilylglycosidic derivatives. Additional work is needed before this method can have a direct clinical application.
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http://dx.doi.org/10.1002/bmc.1130050406 | DOI Listing |
JMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
Immunol Rev
March 2025
Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Roanoke, Virginia, USA.
A robust innate immune response is essential in combating viral pathogens. However, it is equally critical to quell overzealous immune signaling to limit collateral damage and enable inflammation resolution. Pattern recognition receptors are critical regulators of these processes.
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February 2025
Jiangsu Key Laboratory of Immunity and Metabolism, Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Pattern recognition receptors (PRRs), consisting of Toll-like receptors, RIG-I-like receptors, cytosolic DNA sensors, and NOD-like receptors, sense exogenous pathogenic molecules and endogenous damage signals to maintain physiological homeostasis. Upon activation, PRRs stimulate the sensitization of nuclear factor κB, mitogen-activated protein kinase, TANK-binding kinase 1-interferon (IFN) regulatory factor, and inflammasome signaling pathways to produce inflammatory factors and IFNs to activate Janus kinase/signal transducer and activator of transcription signaling pathways, resulting in anti-infection, antitumor, and other specific immune responses. Palmitoylation is a crucial type of post-translational modification that reversibly alters the localization, stability, and biological activity of target molecules.
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February 2025
ENI-G, a Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
Cricket song recognition is thought to evolve through modifications of a shared neural network. However, the species has an unusual recognition pattern that challenges this view: females respond to both normal male song pulse periods and periods twice as long. Of the three minimal models tested, only a single-neuron model with an oscillating membrane could explain this unusual behavior.
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January 2025
Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Introduction: Generating physician letters is a time-consuming task in daily clinical practice.
Methods: This study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner within the field of radiation oncology.
Results: Our findings demonstrate that base LLaMA models, without fine-tuning, are inadequate for effectively generating physician letters.
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