are an important model system for research on host-microbe interaction. Their rapid life cycle, short lifespan, and transparent body structure allow simple quantification of microbial load and the influence of microbial exposure on host survival. host-microbe interaction studies typically examine group survival and infection severity at fixed timepoints. Here we present an imaging pipeline, Systematic Imaging of Killing Organisms (SICKO), that allows longitudinal characterization of microbes colonizing isolated , enabling dynamic tracking of tissue colonization and host survival in the same animals. Using SICKO, we show that or gut colonization dramatically shortens lifespan and that immunodeficient animals lacking are more susceptible to colonization but display similar colony growth relative to wild type. SICKO opens new avenues for detailed research into bacterial pathogenesis, the benefits of probiotics, and the role of the microbiome in host health.
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http://dx.doi.org/10.1101/2023.02.17.529009 | DOI Listing |
Curr Pain Headache Rep
January 2025
Department of Neurology, Danish Headache Center, Copenhagen University Hospital - Rigshospitalet, Valdemar Hansens Vej 5, Entrance 1A, 2600 Glostrup, Copenhagen, Denmark.
Purpose Of Review: To evaluate existing functional magnetic resonance imaging (fMRI) studies on post-traumatic headache (PTH) following traumatic brain injury (TBI).
Recent Findings: We conducted a systematic search of PubMed and Embase databases from inception to February 1, 2024. Eligible fMRI studies were required to include adult participants diagnosed with acute or persistent PTH post-TBI in accordance with any edition of the International Classification of Headache Disorders.
J Neurooncol
January 2025
Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, 100093, China.
Purpose: This study aimed to describe the incidence, clinical and pathological features, and outcomes of H3 K27M- mutant Diffuse Midline Glioma (DMG) patients with leptomeningeal dissemination (LMD) and systematically investigate the predictive and prognostic factors to clarify the response to treatment after the onset of LMD.
Methods: A total of 304 patients diagnosed with DMG from October 17, 2017, to October 17, 2023, were enrolled in this study, of which 32 patients were diagnosed with LMD. Logistic regression analyses were conducted to identify the predictors of LMD, including clinical, molecular, and imaging data.
Insights Imaging
January 2025
Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Objectives: Renal cell carcinoma (RCC) with extrarenal fat (perinephric or renal sinus fat) invasion is the main evidence for the T3a stage. Currently, computed tomography (CT) is still the primary modality for staging RCC. This study aims to determine the diagnostic performance of CT in RCC patients with extrarenal fat invasion.
View Article and Find Full Text PDFMAGMA
January 2025
Translational Research Imaging Center (TRIC), Clinic of Radiology, University of Münster, Albert-Schweitzer-Campus 1, building A16, 48149, Münster, Germany.
Objective: Invasive multimodal fMRI in rodents is often compromised by susceptibility artifacts from adhesives used to secure cranial implants. We hypothesized that adhesive type, shape, and field strength significantly affect susceptibility artifacts, and systematically evaluated various adhesives.
Materials And Methods: Thirty-one adhesives were applied in constrained/unconstrained geometries and imaged with T2*-weighted EPI at 7.
Int J Comput Assist Radiol Surg
January 2025
Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058, Erlangen, Bayern, Germany.
Purpose: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, attention-guided erasing (AGE), across various transfer learning classification tasks for breast abnormality classification in mammography.
Methods: AGE utilizes attention head visualizations from DINO self-supervised pretraining to weakly localize regions of interest (ROI) in images.
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