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Fluorescent biosensors offer a powerful tool for tracking and quantifying protein activity in living systems with high temporospatial resolution. However, the expression of genetically encoded fluorescent proteins can interfere with endogenous signaling pathways, potentially leading to developmental and physiological abnormalities. The EKAREV-NLS mouse model, which carries a FRET-based biosensor for monitoring extracellular signal-regulated kinase (ERK) activity, has been widely utilized both in vivo and in vitro across various cell types and organs.

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Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.

Neuroradiology

January 2025

Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.

Introduction: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.

Methods: A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging.

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Purpose: Pulmonary MRI faces challenges due to low proton density, rapid transverse magnetization decay, and cardiac and respiratory motion. The fermat-looped orthogonally encoded trajectories (FLORET) sequence addresses these issues with high sampling efficiency, strong signal, and motion robustness, but has not yet been applied to phase-resolved functional lung (PREFUL) MRI-a contrast-free method for assessing pulmonary ventilation during free breathing. This study aims to develop a reconstruction pipeline for FLORET UTE, enhancing spatial resolution for three-dimensional (3D) PREFUL ventilation analysis.

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In response to the increasing emergence of zoonotic pathogens, flexible, multisectoral surveillance systems capable of generating alerts thanks to rapid, nonspecific detection, are crucial before pathogens reach human populations. Syndromic surveillance has proven to be a breakthrough for near real-time disease surveillance in the public health sector. It relies on existing nonspecific data, usually collected for other purposes.

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Significance: The precise identification and preservation of functional brain areas during neurosurgery are crucial for optimizing surgical outcomes and minimizing postoperative deficits. Intraoperative imaging plays a vital role in this context, offering insights that guide surgeons in protecting critical cortical regions.

Aim: We aim to evaluate and compare the efficacy of intraoperative thermal imaging (ITI) and intraoperative optical imaging (IOI) in detecting the primary somatosensory cortex, providing a detailed assessment of their potential integration into surgical practice.

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