Publications by authors named "L R Poisson"

We present the design of a VMI spectrometer optimized for attosecond spectroscopy in the 0-40 eV energy range. It is based on a compact three-electrode configuration where the lens shape, size, and material have been optimized using numerical simulations to improve the spectral resolution by a factor of ∼5 relative to the initial design [Eppink and Parker, Rev. Sci.

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Background: Glioblastoma is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.

Methods: We developed a highly reproducible, personalized prognostication and clinical subgrouping system using machine learning (ML) on routine clinical data, MRI, and molecular measures from 2,838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).

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CD19-targeted chimeric antigen receptor-modified T (CAR-T) cells have shown success in clinical studies, with several CD19 CAR-T cell products now having been approved for market use. However, this cell therapy can be associated with side effects such as cytokine release syndrome (CRS). Therefore, pre-clinical test systems are highly desired to permit the evaluation of these unwanted effects before clinical trials begin.

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Although medical mistrust is associated with lower cancer screening uptake among racial minorities, such as African Americans, potential impacts on cancer screening among White Americans are generally understudied. In this study, we examined links from medical mistrust to lung cancer screening among African American ( = 203) and White American ( = 201) smokers. Participants completed the Group-Based Medical Mistrust Scale and viewed a brief online educational module about lung cancer risks, etiology, and screening.

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Article Synopsis
  • Multiple sclerosis (MS) is difficult to diagnose and manage, often leading to late treatment; however, artificial intelligence (AI) shows promise in analyzing patient data to improve diagnosis.* -
  • This study employed a machine-learning approach to analyze metabolite profiles in MS patients and healthy controls, uncovering unique biochemical changes linked to disease severity.* -
  • A trained AI model achieved high accuracy rates (87% overall, with good sensitivity, specificity, and precision), indicating potential for clinical use, but further validation with larger studies is required.*
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